magnet:?xt=urn:btih:31443EA2CA2960FA2557010CCC3D55E9EBF76D83
mixture-models/02_maximum-likelihood-estimation-for-mixture-models/01_the-em-algorithm-for-mixture-models/02_em-for-location-mixtures-of-gaussians.mp4 127.6 MBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/04_the-ar-1-maximum-likelihood-estimation-and-bayesian-inference/02_maximum-likelihood-estimation-in-the-ar-1.mp4 127.2 MBmixture-models/04_applications-of-mixture-models/02_clustering/01_mixture-models-for-clustering.mp4 112.9 MBmixture-models/01_basic-concepts-on-mixture-models/03_definition-of-mixture-models/01_basic-definitions.mp4 109.1 MBbayesian-statistics-capstone/03_bayesian-location-mixture-of-ar-p-model/01_week-3/02_full-conditional-distributions-of-model-parameters.mp4 107.7 MBmixture-models/05_practical-considerations/02_determining-the-number-of-components-in-a-mixture-model/05_estimating-the-full-partition-structure-in-bayesian-settings.mp4 106.8 MBmixture-models/02_maximum-likelihood-estimation-for-mixture-models/01_the-em-algorithm-for-mixture-models/01_em-for-general-mixtures.mp4 103.1 MBmixture-models/04_applications-of-mixture-models/03_classification/01_mixture-models-and-naive-bayes-classifiers.mp4 95.8 MBmixture-models/03_bayesian-estimation-for-mixture-models/01_markov-chain-monte-carlo-algorithms-for-mixture-models/03_mcmc-for-location-mixtures-of-normals-part-1.mp4 90.8 MBmixture-models/04_applications-of-mixture-models/03_classification/02_linear-and-quadratic-discriminant-analysis-in-the-context-of-mixture-models.mp4 87.9 MBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/01_the-general-ar-p-process/01_definition-and-state-space-representation.mp4 83.7 MBbayesian-statistics-time-series-analysis/04_week-4-normal-dynamic-linear-models-part-ii/02_bayesian-inference-in-the-ndlm-part-ii/03_specifying-the-system-covariance-matrix-via-discount-factors.mp4 80.2 MBmixture-models/03_bayesian-estimation-for-mixture-models/01_markov-chain-monte-carlo-algorithms-for-mixture-models/02_markov-chain-monte-carlo-algorithms-part-2.mp4 79.3 MBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/02_bayesian-inference-in-the-ndlm-part-i/01_filtering.mp4 73.9 MBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/01_the-general-ar-p-process/02_examples.mp4 72.2 MBmixture-models/03_bayesian-estimation-for-mixture-models/01_markov-chain-monte-carlo-algorithms-for-mixture-models/01_markov-chain-monte-carlo-algorithms-part-1.mp4 71.5 MBmixture-models/03_bayesian-estimation-for-mixture-models/01_markov-chain-monte-carlo-algorithms-for-mixture-models/04_mcmc-for-location-mixtures-of-normals-part-2.mp4 70.5 MBbayesian-statistics-time-series-analysis/04_week-4-normal-dynamic-linear-models-part-ii/01_seasonal-ndlms/01_fourier-representation.mp4 66.1 MBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/01_the-normal-dynamic-linear-model-definition-model-classes-and-the-superposition/01_ndlm-definition.mp4 64.1 MBmixture-models/01_basic-concepts-on-mixture-models/04_likelihood-function-for-mixture-models/05_parameter-identifiability.mp4 63.2 MBbayesian-statistics-capstone/01_bayesian-conjugate-analysis-for-autogressive-time-series-models/01_week-1/04_model-formulation.mp4 62.9 MBmixture-models/04_applications-of-mixture-models/01_density-estimation/01_density-estimation-using-mixture-models.mp4 61.0 MBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/03_the-ar-1-process-definition-and-properties/01_the-ar-1.mp4 60.9 MBmixture-models/01_basic-concepts-on-mixture-models/04_likelihood-function-for-mixture-models/04_the-likelihood-function.mp4 59.1 MBmixture-models/05_practical-considerations/02_determining-the-number-of-components-in-a-mixture-model/04_estimating-the-number-of-components-in-bayesian-settings.mp4 58.1 MBmixture-models/05_practical-considerations/02_determining-the-number-of-components-in-a-mixture-model/01_bayesian-information-criteria-bic.mp4 56.9 MBbayesian-statistics/03_priors-and-models-for-discrete-data/03_bernoulli-binomial-data/05_data-analysis-example-in-excel.mp4 54.5 MBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/01_the-normal-dynamic-linear-model-definition-model-classes-and-the-superposition/02_polynomial-trend-models.mp4 51.6 MBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/02_4-metropolis-hastings/02_demonstration.mp4 51.2 MBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/04_5-gibbs-sampling/02_conditionally-conjugate-prior-example-with-normal-likelihood.mp4 50.0 MBbayesian-statistics-time-series-analysis/04_week-4-normal-dynamic-linear-models-part-ii/03_case-studies/01_eeg-data.mp4 49.3 MBbayesian-statistics-time-series-analysis/04_week-4-normal-dynamic-linear-models-part-ii/03_case-studies/02_google-trends.mp4 48.6 MBmcmc-bayesian-statistics/03_common-statistical-models/03_8-anova/01_introduction-to-anova.mp4 47.8 MBbayesian-statistics/02_statistical-inference/03_bayesian-inference/05_lesson-5-4-posterior-intervals.mp4 46.9 MBbayesian-statistics/03_priors-and-models-for-discrete-data/04_poisson-data/01_lesson-8-1-poisson-data.mp4 46.3 MBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/03_2-bayesian-modeling/03_posterior-derivation.mp4 46.1 MBbayesian-statistics/04_models-for-continuous-data/04_alternative-priors/01_lesson-11-1-non-informative-priors.mp4 45.5 MBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/02_4-metropolis-hastings/01_algorithm.mp4 44.9 MBmixture-models/05_practical-considerations/01_computational-considerations-for-mixture-models/01_numerical-stability.mp4 43.6 MBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/03_2-bayesian-modeling/01_components-of-bayesian-models.mp4 42.7 MBbayesian-statistics-capstone/02_model-selection-criteria/01_week-2/01_aic-and-bic-in-selecting-the-order-of-ar-process.mp4 42.7 MBbayesian-statistics/02_statistical-inference/02_frequentist-inference/03_lesson-4-2-likelihood-function-and-maximum-likelihood.mp4 42.5 MBbayesian-statistics/03_priors-and-models-for-discrete-data/03_bernoulli-binomial-data/03_lesson-7-3-posterior-mean-and-effective-sample-size.mp4 42.4 MBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/02_bayesian-inference-in-the-ndlm-part-i/03_filtering-in-the-ndlm-example.mp4 41.0 MBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/04_the-ar-1-maximum-likelihood-estimation-and-bayesian-inference/04_bayesian-inference-in-the-ar-1.mp4 40.7 MBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/03_11-hierarchical-modeling/01_correlated-data.mp4 40.6 MBmcmc-bayesian-statistics/03_common-statistical-models/03_8-anova/02_one-way-model-using-jags.mp4 40.3 MBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/04_5-gibbs-sampling/01_multiple-parameter-sampling-and-full-conditional-distributions.mp4 39.8 MBbayesian-statistics/02_statistical-inference/03_bayesian-inference/03_lesson-5-2-inference-example-bayesian.mp4 39.8 MBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/04_3-monte-carlo-estimation/01_monte-carlo-integration.mp4 39.8 MBmcmc-bayesian-statistics/03_common-statistical-models/04_9-logistic-regression/02_jags-model-logistic-regression.mp4 38.7 MBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/02_1-statistical-modeling/02_modeling-process.mp4 38.7 MBbayesian-statistics/04_models-for-continuous-data/05_linear-regression/02_linear-regression-in-r.mp4 38.0 MBbayesian-statistics/04_models-for-continuous-data/05_linear-regression/03_linear-regression-in-excel-analysis-toolpak.mp4 37.8 MBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/02_4-metropolis-hastings/04_random-walk-example-part-2.mp4 37.7 MBbayesian-statistics/01_probability-and-bayes-theorem/03_bayes-theorem/02_lesson-2-2-bayes-theorem.mp4 37.2 MBbayesian-statistics-time-series-analysis/04_week-4-normal-dynamic-linear-models-part-ii/02_bayesian-inference-in-the-ndlm-part-ii/04_ndlm-unknown-observational-variance-example.mp4 37.2 MBbayesian-statistics/04_models-for-continuous-data/05_linear-regression/04_linear-regression-in-excel-statplus-by-analystsoft.mp4 36.9 MBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/02_10-poisson-regression/02_jags-model-poisson-regression.mp4 36.7 MBbayesian-statistics-time-series-analysis/04_week-4-normal-dynamic-linear-models-part-ii/01_seasonal-ndlms/03_building-ndlms-with-multiple-components-examples.mp4 36.5 MBbayesian-statistics/03_priors-and-models-for-discrete-data/03_bernoulli-binomial-data/04_data-analysis-example-in-r.mp4 36.4 MBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/02_bayesian-inference-in-the-ar-p/04_example-bayesian-inference-in-the-ar-p-conditional-likelihood.mp4 36.3 MBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/03_2-bayesian-modeling/04_non-conjugate-models.mp4 35.8 MBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/03_2-bayesian-modeling/02_model-specification.mp4 35.8 MBbayesian-statistics-capstone/01_bayesian-conjugate-analysis-for-autogressive-time-series-models/01_week-1/08_prediction-for-ar-models.mp4 35.1 MBmcmc-bayesian-statistics/03_common-statistical-models/02_7-linear-regression/04_model-checking.mp4 34.8 MBmcmc-bayesian-statistics/03_common-statistical-models/02_7-linear-regression/01_introduction-to-linear-regression.mp4 34.7 MBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/02_bayesian-inference-in-the-ar-p/01_bayesian-inference-in-the-ar-p-reference-prior-conditional-likelihood.mp4 34.7 MBmixture-models/01_basic-concepts-on-mixture-models/04_likelihood-function-for-mixture-models/01_hierarchical-representations.mp4 33.9 MBbayesian-statistics/01_probability-and-bayes-theorem/02_probability/02_lesson-1-1-classical-and-frequentist-probability.mp4 33.9 MBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/02_1-statistical-modeling/01_objectives.mp4 33.5 MBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/02_stationarity-the-acf-and-the-pacf/02_the-autocorrelation-function-acf.mp4 33.2 MBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/03_11-hierarchical-modeling/06_linear-regression-example.mp4 32.6 MBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/01_the-general-ar-p-process/04_simulating-data-from-an-ar-p.mp4 32.1 MBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/05_6-assessing-convergence/01_trace-plots-autocorrelation.mp4 32.0 MBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/04_3-monte-carlo-estimation/03_computing-examples.mp4 31.8 MBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/04_5-gibbs-sampling/03_computing-example-with-normal-likelihood.mp4 31.6 MBbayesian-statistics/01_probability-and-bayes-theorem/04_review-of-distributions/02_lesson-3-2-uniform-distribution.mp4 31.5 MBbayesian-statistics/01_probability-and-bayes-theorem/04_review-of-distributions/01_lesson-3-1-bernoulli-and-binomial-distributions.mp4 31.5 MBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/01_the-normal-dynamic-linear-model-definition-model-classes-and-the-superposition/03_regression-models.mp4 30.5 MBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/01_the-general-ar-p-process/03_acf-of-the-ar-p.mp4 30.5 MBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/02_stationarity-the-acf-and-the-pacf/05_acf-pacf-differencing-and-smoothing-examples.mp4 30.3 MBmixture-models/05_practical-considerations/02_determining-the-number-of-components-in-a-mixture-model/06_example-bayesian-inference-for-the-partition-structure.mp4 30.2 MBbayesian-statistics/02_statistical-inference/02_frequentist-inference/02_lesson-4-1-confidence-intervals.mp4 29.5 MBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/02_stationarity-the-acf-and-the-pacf/01_stationarity.mp4 29.4 MBmcmc-bayesian-statistics/03_common-statistical-models/04_9-logistic-regression/03_prediction.mp4 29.4 MBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/04_3-monte-carlo-estimation/02_monte-carlo-error-and-marginalization.mp4 29.2 MBbayesian-statistics/01_probability-and-bayes-theorem/03_bayes-theorem/01_lesson-2-1-conditional-probability.mp4 28.9 MBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/01_introduction/01_welcome-to-bayesian-statistics-time-series.mp4 28.8 MBbayesian-statistics/03_priors-and-models-for-discrete-data/02_priors/02_lesson-6-2-prior-predictive-binomial-example.mp4 28.6 MBmcmc-bayesian-statistics/03_common-statistical-models/04_9-logistic-regression/01_introduction-to-logistic-regression.mp4 28.3 MBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/04_mixture-models/02_mixture-model-in-jags.mp4 27.8 MBbayesian-statistics/03_priors-and-models-for-discrete-data/03_bernoulli-binomial-data/02_lesson-7-2-conjugate-priors.mp4 27.3 MBbayesian-statistics/02_statistical-inference/02_frequentist-inference/05_lesson-4-4-computing-the-mle-examples.mp4 26.6 MBbayesian-statistics/02_statistical-inference/03_bayesian-inference/02_lesson-5-1-inference-example-frequentist.mp4 26.4 MBmixture-models/05_practical-considerations/01_computational-considerations-for-mixture-models/03_computational-issues-associated-with-multimodality.mp4 26.3 MBbayesian-statistics-capstone/03_bayesian-location-mixture-of-ar-p-model/01_week-3/03_coding-the-gibbs-sampler.mp4 26.0 MBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/02_bayesian-inference-in-the-ar-p/03_model-order-selection.mp4 25.6 MBbayesian-statistics/02_statistical-inference/03_bayesian-inference/04_lesson-5-3-continuous-version-of-bayes-theorem.mp4 25.6 MBmixture-models/02_maximum-likelihood-estimation-for-mixture-models/01_the-em-algorithm-for-mixture-models/05_em-example-2.mp4 25.4 MBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/03_11-hierarchical-modeling/07_linear-regression-example-in-jags.mp4 25.3 MBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/03_11-hierarchical-modeling/04_jags-model-and-model-checking-hierarchical-modeling.mp4 25.3 MBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/02_bayesian-inference-in-the-ndlm-part-i/10_using-the-dlm-package-in-r.mp4 24.6 MBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/01_the-normal-dynamic-linear-model-definition-model-classes-and-the-superposition/05_the-superposition-principle.mp4 24.1 MBmixture-models/03_bayesian-estimation-for-mixture-models/01_markov-chain-monte-carlo-algorithms-for-mixture-models/07_mcmc-example-2.mp4 23.7 MBmixture-models/02_maximum-likelihood-estimation-for-mixture-models/01_the-em-algorithm-for-mixture-models/03_em-example-1.mp4 23.1 MBmixture-models/04_applications-of-mixture-models/02_clustering/02_clustering-example.mp4 23.0 MBmixture-models/04_applications-of-mixture-models/03_classification/03_classification-example.mp4 22.6 MBbayesian-statistics/04_models-for-continuous-data/03_normal-data/01_lesson-10-1-normal-likelihood-with-variance-known.mp4 22.3 MBbayesian-statistics/03_priors-and-models-for-discrete-data/02_priors/03_lesson-6-3-posterior-predictive-distribution.mp4 22.1 MBbayesian-statistics/04_models-for-continuous-data/02_exponential-data/01_lesson-9-1-exponential-data.mp4 21.9 MBmcmc-bayesian-statistics/03_common-statistical-models/02_7-linear-regression/03_jags-model-linear-regression.mp4 21.8 MBmixture-models/03_bayesian-estimation-for-mixture-models/01_markov-chain-monte-carlo-algorithms-for-mixture-models/05_mcmc-example-1.mp4 21.8 MBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/03_the-ar-1-process-definition-and-properties/03_simulating-from-an-ar-1-process.mp4 21.7 MBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/03_jags/02_model-writing-running-and-post-processing.mp4 21.6 MBmixture-models/04_applications-of-mixture-models/01_density-estimation/02_density-estimation-example.mp4 21.6 MBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/02_4-metropolis-hastings/03_random-walk-example-part-1.mp4 21.4 MBmcmc-bayesian-statistics/03_common-statistical-models/02_7-linear-regression/05_alternative-models.mp4 21.4 MBbayesian-statistics-capstone/02_model-selection-criteria/01_week-2/03_deviance-information-criterion-dic.mp4 21.3 MBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/04_3-monte-carlo-estimation/04_computing-monte-carlo-error.mp4 21.1 MBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/02_bayesian-inference-in-the-ndlm-part-i/09_second-order-polynomial-filtering-and-smoothing-example.mp4 20.9 MBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/01_module-overview/01_course-introduction.mp4 20.9 MBmixture-models/01_basic-concepts-on-mixture-models/03_definition-of-mixture-models/06_zero-inflated-mixtures.mp4 20.5 MBbayesian-statistics/03_priors-and-models-for-discrete-data/02_priors/01_lesson-6-1-priors-and-prior-predictive-distributions.mp4 20.5 MBmcmc-bayesian-statistics/03_common-statistical-models/02_7-linear-regression/02_setup-in-r.mp4 20.4 MBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/02_10-poisson-regression/03_predictive-distributions.mp4 20.3 MBbayesian-statistics/03_priors-and-models-for-discrete-data/03_bernoulli-binomial-data/01_lesson-7-1-bernoulli-binomial-likelihood-with-uniform-prior.mp4 20.2 MBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/02_bayesian-inference-in-the-ndlm-part-i/05_smoothing-and-forecasting.mp4 19.6 MBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/02_bayesian-inference-in-the-ndlm-part-i/07_smoothing-in-the-ndlm-example.mp4 19.6 MBmixture-models/05_practical-considerations/02_determining-the-number-of-components-in-a-mixture-model/02_bayesian-information-criteria-example.mp4 19.5 MBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/03_11-hierarchical-modeling/03_prior-predictive-simulation.mp4 18.9 MBbayesian-statistics/01_probability-and-bayes-theorem/02_probability/03_lesson-1-2-bayesian-probability-and-coherence.mp4 18.8 MBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/02_bayesian-inference-in-the-ar-p/07_spectral-representation-of-the-ar-p.mp4 18.7 MBbayesian-statistics-capstone/01_bayesian-conjugate-analysis-for-autogressive-time-series-models/01_week-1/01_introduction.mp4 18.7 MBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/05_6-assessing-convergence/03_multiple-chains-burn-in-gelman-rubin-diagnostic.mp4 18.2 MBbayesian-statistics/02_statistical-inference/02_frequentist-inference/04_lesson-4-3-computing-the-mle.mp4 18.2 MBbayesian-statistics/04_models-for-continuous-data/03_normal-data/02_lesson-10-2-normal-likelihood-with-variance-unknown.mp4 18.2 MBbayesian-statistics-time-series-analysis/04_week-4-normal-dynamic-linear-models-part-ii/02_bayesian-inference-in-the-ndlm-part-ii/01_filtering-smoothing-and-forecasting-unknown-observational-variance.mp4 18.1 MBbayesian-statistics/01_probability-and-bayes-theorem/04_review-of-distributions/03_lesson-3-3-exponential-and-normal-distributions.mp4 18.0 MBmixture-models/01_basic-concepts-on-mixture-models/03_definition-of-mixture-models/02_mixtures-of-gaussians.mp4 17.4 MBbayesian-statistics/04_models-for-continuous-data/04_alternative-priors/02_lesson-11-2-jeffreys-prior.mp4 16.7 MBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/02_10-poisson-regression/01_introduction-to-poisson-regression.mp4 15.7 MBbayesian-statistics-capstone/03_bayesian-location-mixture-of-ar-p-model/01_week-3/01_prediction-for-location-mixture-of-ar-models.mp4 14.6 MBbayesian-statistics-capstone/03_bayesian-location-mixture-of-ar-p-model/01_week-3/06_prediction-for-location-mixture-of-ar-model.mp4 14.6 MBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/02_bayesian-inference-in-the-ar-p/08_spectral-representation-of-the-ar-p-example.mp4 14.3 MBbayesian-statistics/01_probability-and-bayes-theorem/01_module-overview/01_course-introduction.mp4 14.2 MBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/03_11-hierarchical-modeling/05_posterior-predictive-simulation.mp4 13.7 MBbayesian-statistics/02_statistical-inference/02_frequentist-inference/07_introduction-to-r.mp4 13.7 MBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/03_jags/01_download-install-setup.mp4 12.1 MBmixture-models/01_basic-concepts-on-mixture-models/02_the-r-environment-for-statistical-computing/01_installing-and-using-r.mp4 10.6 MBbayesian-statistics/02_statistical-inference/02_frequentist-inference/09_plotting-the-likelihood-in-excel.mp4 9.4 MBmixture-models/01_basic-concepts-on-mixture-models/01_introduction/01_welcome-to-bayesian-statistics-mixture-models.mp4 9.3 MBmixture-models/01_basic-concepts-on-mixture-models/04_likelihood-function-for-mixture-models/02_sampling-from-a-mixture-model.mp4 9.2 MBbayesian-statistics/02_statistical-inference/02_frequentist-inference/08_plotting-the-likelihood-in-r.mp4 9.0 MBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/04_the-ar-1-maximum-likelihood-estimation-and-bayesian-inference/05_bayesian-inference-in-the-ar-1-conditional-likelihood-example.mp4 8.8 MBmcmc-bayesian-statistics/03_common-statistical-models/02_7-linear-regression/06_deviance-information-criterion-dic.mp4 7.2 MBmcmc-bayesian-statistics/05_capstone-project/01_course-conclusion/01_course-conclusion.mp4 5.2 MB
mcmc-bayesian-statistics/03_common-statistical-models/02_7-linear-regression/07_code-for-lesson-7_lesson_07.html 3.6 MBbayesian-statistics/04_models-for-continuous-data/06_course-conclusion/01_conclusion.mp4 3.3 MBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/05_6-assessing-convergence/04_code-for-lesson-6_lesson_06.html 2.3 MBmcmc-bayesian-statistics/03_common-statistical-models/04_9-logistic-regression/04_code-for-lesson-9_lesson_09.html 1.8 MBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/03_11-hierarchical-modeling/09_code-and-data-for-lesson-11_lesson_11.html 1.7 MBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/02_10-poisson-regression/05_code-for-lesson-10_lesson_10.html 1.6 MBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/02_4-metropolis-hastings/05_code-for-lesson-4_lesson_04.html 1.5 MB
mixture-models/06_Resources/01_notes-on-finite-mixture-models/01__mixturemodels.pdf 1.5 MBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/04_mixture-models/01_mixture-model-introduction-data-and-code_lesson_11mixture.html 1.4 MBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/05_background-for-lesson-4/01_markov-chains_background_MarkovChains.html 1.2 MBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/02_10-poisson-regression/04_prior-sensitivity-analysis_priorSensitivity.html 1.1 MBmcmc-bayesian-statistics/03_common-statistical-models/05_multiple-factor-anova/01_multiple-factor-anova_lesson_08multipleANOVA.html 1.1 MBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/04_5-gibbs-sampling/04_code-for-lesson-5_lesson_05.html 1.0 MBmcmc-bayesian-statistics/03_common-statistical-models/03_8-anova/03_code-for-lesson-8_lesson_08.html 943.2 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/03_jags/04_code-from-jags-introduction_JAGSintro.html 911.5 kBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/04_3-monte-carlo-estimation/05_code-for-lesson-3_lesson_03.html 893.2 kBbayesian-statistics-capstone/01_bayesian-conjugate-analysis-for-autogressive-time-series-models/01_week-1/07_ar-model-fitting-example_instructions.html 663.6 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/01_introduction/02_introduction-to-r_R-intro.pdf 627.0 kBmixture-models/01_basic-concepts-on-mixture-models/02_the-r-environment-for-statistical-computing/02_an-introduction-to-r_R-intro.pdf 627.0 kBbayesian-statistics-capstone/03_bayesian-location-mixture-of-ar-p-model/01_week-3/07_location-and-scale-mixture-of-ar-model_instructions.html 274.1 kBbayesian-statistics/01_probability-and-bayes-theorem/04_review-of-distributions/04_supplementary-material-for-lesson-3_L3_supp.pdf 156.5 kBbayesian-statistics-capstone/01_bayesian-conjugate-analysis-for-autogressive-time-series-models/01_week-1/10_extended-ar-model_Honor-Extend_AR_model.pdf 154.6 kBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/03_2-bayesian-modeling/05_reference-common-probability-distributions_Distributions.pdf 151.3 kBbayesian-statistics/04_models-for-continuous-data/05_linear-regression/01_background-for-lesson-12_L12_background.pdf 143.4 kBbayesian-statistics/02_statistical-inference/03_bayesian-inference/01_background-for-lesson-5_L5_background.pdf 130.0 kBbayesian-statistics/02_statistical-inference/03_bayesian-inference/06_supplementary-material-for-lesson-5_L5_supp.pdf 119.5 kBbayesian-statistics-capstone/02_model-selection-criteria/01_week-2/02_aic-and-bic-example_instructions.html 117.3 kBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/01_the-general-ar-p-process/07_the-ar-p-review_summary.pdf 116.4 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/05_6-assessing-convergence/02_autocorrelation_Autocorrelation.pdf 108.1 kBbayesian-statistics-time-series-analysis/04_week-4-normal-dynamic-linear-models-part-ii/02_bayesian-inference-in-the-ndlm-part-ii/02_summary-of-filtering-smoothing-and-forecasting-distributions-ndlm-unknown_ndlm_filtering_smoothing_forecasting_unknown_v.pdf 107.7 kBbayesian-statistics/02_statistical-inference/02_frequentist-inference/06_supplementary-material-for-lesson-4_L4_supp-v2.pdf 107.5 kBbayesian-statistics-capstone/02_model-selection-criteria/01_week-2/04_dic-example_instructions.html 105.4 kBbayesian-statistics/02_statistical-inference/02_frequentist-inference/01_background-for-lesson-4_L4_background.pdf 102.9 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/03_11-hierarchical-modeling/02_normal-hierarchical-model_hierarhcicalNormal.pdf 95.6 kBbayesian-statistics-time-series-analysis/04_week-4-normal-dynamic-linear-models-part-ii/01_seasonal-ndlms/04_summary-dlm-fourier-representation_seasonal_fourier_dlm.pdf 94.2 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/04_the-ar-1-maximum-likelihood-estimation-and-bayesian-inference/01_review-of-maximum-likelihood-and-bayesian-inference-in-regression_Bayesian_regression_review.pdf 92.4 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/01_the-normal-dynamic-linear-model-definition-model-classes-and-the-superposition/04_summary-of-polynomial-trend-and-regression-models_polynomial_regression_ndlms.pdf 90.1 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/04_the-ar-1-maximum-likelihood-estimation-and-bayesian-inference/07_bayesian-inference-in-the-ar-1-full-likelihood-example_bayesian_ar1_full_likelihood_example.pdf 86.6 kBbayesian-statistics/01_probability-and-bayes-theorem/02_probability/01_background-for-lesson-1_L1_background.pdf 86.5 kBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/02_bayesian-inference-in-the-ar-p/10_arima-processes_ARIMA.pdf 83.8 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/02_stationarity-the-acf-and-the-pacf/04_differencing-and-smoothing_differencing_and_smoothing.pdf 83.5 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/03_11-hierarchical-modeling/08_applications-of-hierarchical-modeling_hierarchicalModelApplications.pdf 83.3 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/02_bayesian-inference-in-the-ndlm-part-i/02_summary-of-the-filtering-distributions_ndlm_filtering.pdf 82.2 kBbayesian-statistics/04_models-for-continuous-data/03_normal-data/03_supplementary-material-for-lesson-10_L10_supp.pdf 77.3 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/02_stationarity-the-acf-and-the-pacf/03_the-partial-autocorrelation-function-pacf_pacf.pdf 74.9 kBmcmc-bayesian-statistics/05_capstone-project/01_course-conclusion/02_further-reading-and-acknowledgements_furtherReading_refs.pdf 74.6 kBbayesian-statistics-time-series-analysis/04_week-4-normal-dynamic-linear-models-part-ii/01_seasonal-ndlms/02_fourier-representation-example-1_seasonal_models_example1.pdf 74.1 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/02_bayesian-inference-in-the-ndlm-part-i/06_summary-of-the-smoothing-and-forecasting-distributions_ndlm_smoothing_forecasting.pdf 72.9 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/01_the-normal-dynamic-linear-model-definition-model-classes-and-the-superposition/06_superposition-principle-general-case_superposition_principle.pdf 72.7 kBbayesian-statistics/04_models-for-continuous-data/04_alternative-priors/03_supplementary-material-for-lesson-11_L11_supp.pdf 72.0 kBbayesian-statistics-capstone/01_bayesian-conjugate-analysis-for-autogressive-time-series-models/01_week-1/09_ar-model-prediction-example_instructions.html 61.8 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/03_the-ar-1-process-definition-and-properties/02_the-pacf-of-the-ar-1-process_pacf_ar1.pdf 59.3 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/03_jags/03_alternative-mcmc-software_MCMCsoftware.pdf 50.1 kB
mixture-models/04_applications-of-mixture-models/02_clustering/01_mixture-models-for-clustering.en.srt 30.6 kBbayesian-statistics/01_probability-and-bayes-theorem/03_bayes-theorem/03_supplementary-material-for-lesson-2_L2_supp-v2.pdf 29.9 kBmixture-models/01_basic-concepts-on-mixture-models/03_definition-of-mixture-models/01_basic-definitions.en.srt 29.6 kBmixture-models/02_maximum-likelihood-estimation-for-mixture-models/01_the-em-algorithm-for-mixture-models/01_em-for-general-mixtures.en.srt 25.4 kBmixture-models/05_practical-considerations/02_determining-the-number-of-components-in-a-mixture-model/06_example-bayesian-inference-for-the-partition-structure.en.srt 24.5 kBmixture-models/04_applications-of-mixture-models/03_classification/01_mixture-models-and-naive-bayes-classifiers.en.srt 24.2 kBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/01_the-general-ar-p-process/01_definition-and-state-space-representation.en.srt 24.1 kBmixture-models/02_maximum-likelihood-estimation-for-mixture-models/01_the-em-algorithm-for-mixture-models/02_em-for-location-mixtures-of-gaussians.en.srt 23.3 kBmixture-models/05_practical-considerations/02_determining-the-number-of-components-in-a-mixture-model/05_estimating-the-full-partition-structure-in-bayesian-settings.en.srt 22.7 kBbayesian-statistics-time-series-analysis/04_week-4-normal-dynamic-linear-models-part-ii/03_case-studies/01_eeg-data.en.srt 22.7 kBmixture-models/03_bayesian-estimation-for-mixture-models/01_markov-chain-monte-carlo-algorithms-for-mixture-models/03_mcmc-for-location-mixtures-of-normals-part-1.en.srt 22.1 kBmixture-models/05_practical-considerations/01_computational-considerations-for-mixture-models/01_numerical-stability.en.srt 21.7 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/02_bayesian-inference-in-the-ndlm-part-i/03_filtering-in-the-ndlm-example.en.srt 20.0 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/04_the-ar-1-maximum-likelihood-estimation-and-bayesian-inference/02_maximum-likelihood-estimation-in-the-ar-1.en.srt 19.2 kBbayesian-statistics-time-series-analysis/04_week-4-normal-dynamic-linear-models-part-ii/03_case-studies/02_google-trends.en.srt 19.2 kBmcmc-bayesian-statistics/03_common-statistical-models/04_9-logistic-regression/02_jags-model-logistic-regression.en.srt 19.1 kBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/01_the-general-ar-p-process/02_examples.en.srt 19.1 kBmcmc-bayesian-statistics/03_common-statistical-models/03_8-anova/02_one-way-model-using-jags.en.srt 19.0 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/01_the-normal-dynamic-linear-model-definition-model-classes-and-the-superposition/01_ndlm-definition.en.srt 18.9 kBbayesian-statistics-time-series-analysis/04_week-4-normal-dynamic-linear-models-part-ii/02_bayesian-inference-in-the-ndlm-part-ii/04_ndlm-unknown-observational-variance-example.en.srt 18.7 kBbayesian-statistics-time-series-analysis/04_week-4-normal-dynamic-linear-models-part-ii/01_seasonal-ndlms/01_fourier-representation.en.srt 18.6 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/02_10-poisson-regression/02_jags-model-poisson-regression.en.srt 18.5 kBbayesian-statistics-capstone/03_bayesian-location-mixture-of-ar-p-model/01_week-3/02_full-conditional-distributions-of-model-parameters.en.srt 18.4 kBmixture-models/01_basic-concepts-on-mixture-models/04_likelihood-function-for-mixture-models/04_the-likelihood-function.en.srt 18.4 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/02_bayesian-inference-in-the-ndlm-part-i/01_filtering.en.srt 18.3 kBbayesian-statistics-time-series-analysis/04_week-4-normal-dynamic-linear-models-part-ii/02_bayesian-inference-in-the-ndlm-part-ii/03_specifying-the-system-covariance-matrix-via-discount-factors.en.srt 18.3 kBbayesian-statistics/04_models-for-continuous-data/05_linear-regression/02_linear-regression-in-r.en.srt 18.3 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/02_4-metropolis-hastings/04_random-walk-example-part-2.en.srt 18.2 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/05_6-assessing-convergence/01_trace-plots-autocorrelation.en.srt 17.9 kBmixture-models/04_applications-of-mixture-models/03_classification/02_linear-and-quadratic-discriminant-analysis-in-the-context-of-mixture-models.en.srt 17.5 kBbayesian-statistics/03_priors-and-models-for-discrete-data/03_bernoulli-binomial-data/05_data-analysis-example-in-excel.en.srt 17.4 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/03_the-ar-1-process-definition-and-properties/01_the-ar-1.en.srt 17.2 kBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/01_the-general-ar-p-process/04_simulating-data-from-an-ar-p.en.srt 16.9 kBmcmc-bayesian-statistics/03_common-statistical-models/02_7-linear-regression/04_model-checking.en.srt 16.8 kBmixture-models/03_bayesian-estimation-for-mixture-models/01_markov-chain-monte-carlo-algorithms-for-mixture-models/02_markov-chain-monte-carlo-algorithms-part-2.en.srt 16.7 kBmixture-models/04_applications-of-mixture-models/01_density-estimation/01_density-estimation-using-mixture-models.en.srt 16.6 kBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/02_bayesian-inference-in-the-ar-p/04_example-bayesian-inference-in-the-ar-p-conditional-likelihood.en.srt 16.5 kBmixture-models/03_bayesian-estimation-for-mixture-models/01_markov-chain-monte-carlo-algorithms-for-mixture-models/01_markov-chain-monte-carlo-algorithms-part-1.en.srt 16.5 kBbayesian-statistics/04_models-for-continuous-data/05_linear-regression/04_linear-regression-in-excel-statplus-by-analystsoft.en.srt 16.4 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/04_5-gibbs-sampling/03_computing-example-with-normal-likelihood.en.srt 16.2 kB
mixture-models/04_applications-of-mixture-models/02_clustering/01_mixture-models-for-clustering.en.txt 16.0 kBmcmc-bayesian-statistics/03_common-statistical-models/04_9-logistic-regression/03_prediction.en.srt 16.0 kBmixture-models/01_basic-concepts-on-mixture-models/03_definition-of-mixture-models/06_zero-inflated-mixtures.en.srt 16.0 kBmixture-models/02_maximum-likelihood-estimation-for-mixture-models/01_the-em-algorithm-for-mixture-models/05_em-example-2.en.srt 15.9 kBmixture-models/02_maximum-likelihood-estimation-for-mixture-models/01_the-em-algorithm-for-mixture-models/03_em-example-1.en.srt 15.8 kBmixture-models/05_practical-considerations/02_determining-the-number-of-components-in-a-mixture-model/02_bayesian-information-criteria-example.en.srt 15.8 kBbayesian-statistics/03_priors-and-models-for-discrete-data/03_bernoulli-binomial-data/04_data-analysis-example-in-r.en.srt 15.7 kBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/04_3-monte-carlo-estimation/03_computing-examples.en.srt 15.5 kBmixture-models/01_basic-concepts-on-mixture-models/03_definition-of-mixture-models/01_basic-definitions.en.txt 15.4 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/02_stationarity-the-acf-and-the-pacf/05_acf-pacf-differencing-and-smoothing-examples.en.srt 15.2 kBmixture-models/05_practical-considerations/01_computational-considerations-for-mixture-models/03_computational-issues-associated-with-multimodality.en.srt 15.0 kBmixture-models/03_bayesian-estimation-for-mixture-models/01_markov-chain-monte-carlo-algorithms-for-mixture-models/04_mcmc-for-location-mixtures-of-normals-part-2.en.srt 14.5 kBmixture-models/03_bayesian-estimation-for-mixture-models/01_markov-chain-monte-carlo-algorithms-for-mixture-models/07_mcmc-example-2.en.srt 14.4 kBmixture-models/01_basic-concepts-on-mixture-models/03_definition-of-mixture-models/02_mixtures-of-gaussians.en.srt 14.4 kBmixture-models/04_applications-of-mixture-models/01_density-estimation/02_density-estimation-example.en.srt 14.0 kBmixture-models/03_bayesian-estimation-for-mixture-models/01_markov-chain-monte-carlo-algorithms-for-mixture-models/05_mcmc-example-1.en.srt 13.9 kBbayesian-statistics/04_models-for-continuous-data/05_linear-regression/03_linear-regression-in-excel-analysis-toolpak.en.srt 13.8 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/04_mixture-models/02_mixture-model-in-jags.en.srt 13.7 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/03_11-hierarchical-modeling/04_jags-model-and-model-checking-hierarchical-modeling.en.srt 13.7 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/02_bayesian-inference-in-the-ndlm-part-i/05_smoothing-and-forecasting.en.srt 13.3 kBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/04_3-monte-carlo-estimation/04_computing-monte-carlo-error.en.srt 13.3 kBmixture-models/04_applications-of-mixture-models/02_clustering/02_clustering-example.en.srt 13.3 kBmixture-models/02_maximum-likelihood-estimation-for-mixture-models/01_the-em-algorithm-for-mixture-models/01_em-for-general-mixtures.en.txt 13.2 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/02_4-metropolis-hastings/03_random-walk-example-part-1.en.srt 13.2 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/01_the-normal-dynamic-linear-model-definition-model-classes-and-the-superposition/02_polynomial-trend-models.en.srt 13.1 kBbayesian-statistics-capstone/02_model-selection-criteria/01_week-2/01_aic-and-bic-in-selecting-the-order-of-ar-process.en.srt 12.9 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/02_4-metropolis-hastings/02_demonstration.en.srt 12.9 kBmixture-models/05_practical-considerations/02_determining-the-number-of-components-in-a-mixture-model/06_example-bayesian-inference-for-the-partition-structure.en.txt 12.7 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/03_jags/02_model-writing-running-and-post-processing.en.srt 12.4 kBmixture-models/01_basic-concepts-on-mixture-models/04_likelihood-function-for-mixture-models/05_parameter-identifiability.en.srt 12.4 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/01_introduction/01_welcome-to-bayesian-statistics-time-series.en.srt 12.4 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/02_10-poisson-regression/03_predictive-distributions.en.srt 12.4 kBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/01_the-general-ar-p-process/01_definition-and-state-space-representation.en.txt 12.3 kBmixture-models/04_applications-of-mixture-models/03_classification/01_mixture-models-and-naive-bayes-classifiers.en.txt 12.3 kBmixture-models/04_applications-of-mixture-models/03_classification/03_classification-example.en.srt 12.2 kBmixture-models/02_maximum-likelihood-estimation-for-mixture-models/01_the-em-algorithm-for-mixture-models/02_em-for-location-mixtures-of-gaussians.en.txt 12.2 kBbayesian-statistics-time-series-analysis/04_week-4-normal-dynamic-linear-models-part-ii/03_case-studies/02_google-trends.en.txt 12.0 kBbayesian-statistics-time-series-analysis/04_week-4-normal-dynamic-linear-models-part-ii/02_bayesian-inference-in-the-ndlm-part-ii/01_filtering-smoothing-and-forecasting-unknown-observational-variance.en.srt 11.9 kBmcmc-bayesian-statistics/03_common-statistical-models/03_8-anova/02_one-way-model-using-jags.en.txt 11.8 kBmixture-models/05_practical-considerations/02_determining-the-number-of-components-in-a-mixture-model/05_estimating-the-full-partition-structure-in-bayesian-settings.en.txt 11.8 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/03_11-hierarchical-modeling/07_linear-regression-example-in-jags.en.srt 11.7 kBbayesian-statistics-time-series-analysis/04_week-4-normal-dynamic-linear-models-part-ii/03_case-studies/01_eeg-data.en.txt 11.7 kBmcmc-bayesian-statistics/03_common-statistical-models/04_9-logistic-regression/02_jags-model-logistic-regression.en.txt 11.7 kBmcmc-bayesian-statistics/03_common-statistical-models/02_7-linear-regression/03_jags-model-linear-regression.en.srt 11.7 kBmixture-models/05_practical-considerations/02_determining-the-number-of-components-in-a-mixture-model/01_bayesian-information-criteria-bic.en.srt 11.5 kBmixture-models/03_bayesian-estimation-for-mixture-models/01_markov-chain-monte-carlo-algorithms-for-mixture-models/03_mcmc-for-location-mixtures-of-normals-part-1.en.txt 11.4 kBmixture-models/05_practical-considerations/01_computational-considerations-for-mixture-models/01_numerical-stability.en.txt 11.3 kBbayesian-statistics-time-series-analysis/04_week-4-normal-dynamic-linear-models-part-ii/02_bayesian-inference-in-the-ndlm-part-ii/05_rcode-ndlm-unknown-observational-variance-example_instructions.html 11.2 kBbayesian-statistics/04_models-for-continuous-data/05_linear-regression/02_linear-regression-in-r.en.txt 11.2 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/04_the-ar-1-maximum-likelihood-estimation-and-bayesian-inference/02_maximum-likelihood-estimation-in-the-ar-1.en.txt 11.1 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/05_6-assessing-convergence/01_trace-plots-autocorrelation.en.txt 11.0 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/03_11-hierarchical-modeling/03_prior-predictive-simulation.en.srt 11.0 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/02_10-poisson-regression/02_jags-model-poisson-regression.en.txt 11.0 kBmixture-models/05_practical-considerations/02_determining-the-number-of-components-in-a-mixture-model/04_estimating-the-number-of-components-in-bayesian-settings.en.srt 10.9 kBbayesian-statistics-time-series-analysis/04_week-4-normal-dynamic-linear-models-part-ii/02_bayesian-inference-in-the-ndlm-part-ii/03_specifying-the-system-covariance-matrix-via-discount-factors.en.txt 10.8 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/04_the-ar-1-maximum-likelihood-estimation-and-bayesian-inference/04_bayesian-inference-in-the-ar-1.en.srt 10.8 kBbayesian-statistics/03_priors-and-models-for-discrete-data/03_bernoulli-binomial-data/05_data-analysis-example-in-excel.en.txt 10.8 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/02_4-metropolis-hastings/04_random-walk-example-part-2.en.txt 10.8 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/02_bayesian-inference-in-the-ndlm-part-i/01_filtering.en.txt 10.7 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/04_5-gibbs-sampling/02_conditionally-conjugate-prior-example-with-normal-likelihood.en.srt 10.6 kBmcmc-bayesian-statistics/03_common-statistical-models/02_7-linear-regression/05_alternative-models.en.srt 10.5 kBbayesian-statistics-capstone/03_bayesian-location-mixture-of-ar-p-model/01_week-3/03_coding-the-gibbs-sampler.en.srt 10.5 kBmcmc-bayesian-statistics/03_common-statistical-models/03_8-anova/01_introduction-to-anova.en.srt 10.4 kBbayesian-statistics/03_priors-and-models-for-discrete-data/04_poisson-data/01_lesson-8-1-poisson-data.en.srt 10.4 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/02_bayesian-inference-in-the-ndlm-part-i/03_filtering-in-the-ndlm-example.en.txt 10.4 kBmixture-models/04_applications-of-mixture-models/03_classification/02_linear-and-quadratic-discriminant-analysis-in-the-context-of-mixture-models.en.txt 10.4 kBmcmc-bayesian-statistics/03_common-statistical-models/02_7-linear-regression/04_model-checking.en.txt 10.3 kBbayesian-statistics/04_models-for-continuous-data/04_alternative-priors/01_lesson-11-1-non-informative-priors.en.srt 10.3 kBbayesian-statistics-capstone/01_bayesian-conjugate-analysis-for-autogressive-time-series-models/01_week-1/04_model-formulation.en.srt 10.2 kBbayesian-statistics/04_models-for-continuous-data/05_linear-regression/04_linear-regression-in-excel-statplus-by-analystsoft.en.txt 10.1 kBmixture-models/02_maximum-likelihood-estimation-for-mixture-models/01_the-em-algorithm-for-mixture-models/05_em-example-2.en.txt 10.0 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/01_the-normal-dynamic-linear-model-definition-model-classes-and-the-superposition/01_ndlm-definition.en.txt 10.0 kBmixture-models/02_maximum-likelihood-estimation-for-mixture-models/01_the-em-algorithm-for-mixture-models/03_em-example-1.en.txt 10.0 kBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/01_the-general-ar-p-process/02_examples.en.txt 9.9 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/02_4-metropolis-hastings/01_algorithm.en.srt 9.9 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/04_5-gibbs-sampling/03_computing-example-with-normal-likelihood.en.txt 9.9 kBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/02_bayesian-inference-in-the-ar-p/03_model-order-selection.en.srt 9.8 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/04_5-gibbs-sampling/01_multiple-parameter-sampling-and-full-conditional-distributions.en.srt 9.8 kBbayesian-statistics-time-series-analysis/04_week-4-normal-dynamic-linear-models-part-ii/01_seasonal-ndlms/01_fourier-representation.en.txt 9.7 kBbayesian-statistics-time-series-analysis/04_week-4-normal-dynamic-linear-models-part-ii/02_bayesian-inference-in-the-ndlm-part-ii/04_ndlm-unknown-observational-variance-example.en.txt 9.7 kBbayesian-statistics/03_priors-and-models-for-discrete-data/03_bernoulli-binomial-data/04_data-analysis-example-in-r.en.txt 9.7 kBmcmc-bayesian-statistics/03_common-statistical-models/04_9-logistic-regression/03_prediction.en.txt 9.7 kBmcmc-bayesian-statistics/03_common-statistical-models/02_7-linear-regression/02_setup-in-r.en.srt 9.7 kBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/03_2-bayesian-modeling/03_posterior-derivation.en.srt 9.6 kBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/04_3-monte-carlo-estimation/01_monte-carlo-integration.en.srt 9.6 kBmixture-models/01_basic-concepts-on-mixture-models/04_likelihood-function-for-mixture-models/04_the-likelihood-function.en.txt 9.6 kBmixture-models/05_practical-considerations/01_computational-considerations-for-mixture-models/03_computational-issues-associated-with-multimodality.en.txt 9.5 kBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/03_2-bayesian-modeling/01_components-of-bayesian-models.en.srt 9.5 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/05_6-assessing-convergence/03_multiple-chains-burn-in-gelman-rubin-diagnostic.en.srt 9.4 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/02_bayesian-inference-in-the-ndlm-part-i/10_using-the-dlm-package-in-r.en.srt 9.4 kBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/04_3-monte-carlo-estimation/03_computing-examples.en.txt 9.4 kBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/02_1-statistical-modeling/02_modeling-process.en.srt 9.4 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/03_the-ar-1-process-definition-and-properties/03_simulating-from-an-ar-1-process.en.srt 9.4 kBbayesian-statistics-capstone/03_bayesian-location-mixture-of-ar-p-model/01_week-3/02_full-conditional-distributions-of-model-parameters.en.txt 9.3 kBmixture-models/03_bayesian-estimation-for-mixture-models/01_markov-chain-monte-carlo-algorithms-for-mixture-models/07_mcmc-example-2.en.txt 9.2 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/03_11-hierarchical-modeling/01_correlated-data.en.srt 9.1 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/02_bayesian-inference-in-the-ndlm-part-i/09_second-order-polynomial-filtering-and-smoothing-example.en.srt 9.0 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/02_bayesian-inference-in-the-ndlm-part-i/11_rcode-using-the-dlm-package-in-r_instructions.html 9.0 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/02_stationarity-the-acf-and-the-pacf/02_the-autocorrelation-function-acf.en.srt 9.0 kBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/01_the-general-ar-p-process/03_acf-of-the-ar-p.en.srt 9.0 kBbayesian-statistics/02_statistical-inference/02_frequentist-inference/07_introduction-to-r.en.srt 8.9 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/03_the-ar-1-process-definition-and-properties/01_the-ar-1.en.txt 8.9 kBmixture-models/03_bayesian-estimation-for-mixture-models/01_markov-chain-monte-carlo-algorithms-for-mixture-models/05_mcmc-example-1.en.txt 8.8 kBmixture-models/03_bayesian-estimation-for-mixture-models/01_markov-chain-monte-carlo-algorithms-for-mixture-models/02_markov-chain-monte-carlo-algorithms-part-2.en.txt 8.8 kBmixture-models/04_applications-of-mixture-models/01_density-estimation/01_density-estimation-using-mixture-models.en.txt 8.7 kBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/01_the-general-ar-p-process/04_simulating-data-from-an-ar-p.en.txt 8.7 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/01_the-normal-dynamic-linear-model-definition-model-classes-and-the-superposition/03_regression-models.en.srt 8.7 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/03_11-hierarchical-modeling/05_posterior-predictive-simulation.en.srt 8.7 kBbayesian-statistics/02_statistical-inference/03_bayesian-inference/05_lesson-5-4-posterior-intervals.en.srt 8.7 kBbayesian-statistics/04_models-for-continuous-data/05_linear-regression/03_linear-regression-in-excel-analysis-toolpak.en.txt 8.7 kBmixture-models/03_bayesian-estimation-for-mixture-models/01_markov-chain-monte-carlo-algorithms-for-mixture-models/01_markov-chain-monte-carlo-algorithms-part-1.en.txt 8.6 kBbayesian-statistics/02_statistical-inference/03_bayesian-inference/03_lesson-5-2-inference-example-bayesian.en.srt 8.6 kBmcmc-bayesian-statistics/03_common-statistical-models/02_7-linear-regression/01_introduction-to-linear-regression.en.srt 8.5 kBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/02_bayesian-inference-in-the-ar-p/04_example-bayesian-inference-in-the-ar-p-conditional-likelihood.en.txt 8.5 kBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/03_2-bayesian-modeling/04_non-conjugate-models.en.srt 8.5 kBbayesian-statistics-time-series-analysis/04_week-4-normal-dynamic-linear-models-part-ii/01_seasonal-ndlms/03_building-ndlms-with-multiple-components-examples.en.srt 8.5 kBmixture-models/04_applications-of-mixture-models/03_classification/04_sample-em-algorithm-for-classification-problems_wine_training.txt 8.5 kBmixture-models/03_bayesian-estimation-for-mixture-models/01_markov-chain-monte-carlo-algorithms-for-mixture-models/04_mcmc-for-location-mixtures-of-normals-part-2.en.txt 8.4 kBmixture-models/01_basic-concepts-on-mixture-models/03_definition-of-mixture-models/06_zero-inflated-mixtures.en.txt 8.4 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/02_bayesian-inference-in-the-ndlm-part-i/04_rcode-filtering-in-the-ndlm-example_instructions.html 8.3 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/04_mixture-models/02_mixture-model-in-jags.en.txt 8.2 kBmixture-models/04_applications-of-mixture-models/02_clustering/02_clustering-example.en.txt 8.2 kBmixture-models/05_practical-considerations/02_determining-the-number-of-components-in-a-mixture-model/02_bayesian-information-criteria-example.en.txt 8.2 kBbayesian-statistics/01_probability-and-bayes-theorem/02_probability/02_lesson-1-1-classical-and-frequentist-probability.en.srt 8.2 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/03_11-hierarchical-modeling/04_jags-model-and-model-checking-hierarchical-modeling.en.txt 8.1 kBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/02_bayesian-inference-in-the-ar-p/01_bayesian-inference-in-the-ar-p-reference-prior-conditional-likelihood.en.srt 8.1 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/01_the-normal-dynamic-linear-model-definition-model-classes-and-the-superposition/05_the-superposition-principle.en.srt 8.1 kBbayesian-statistics-capstone/01_bayesian-conjugate-analysis-for-autogressive-time-series-models/01_week-1/08_prediction-for-ar-models.en.srt 8.1 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/02_bayesian-inference-in-the-ndlm-part-i/05_smoothing-and-forecasting.en.txt 8.0 kBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/04_3-monte-carlo-estimation/04_computing-monte-carlo-error.en.txt 8.0 kBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/02_1-statistical-modeling/01_objectives.en.srt 8.0 kBmixture-models/01_basic-concepts-on-mixture-models/04_likelihood-function-for-mixture-models/01_hierarchical-representations.en.srt 8.0 kBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/03_2-bayesian-modeling/02_model-specification.en.srt 8.0 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/01_the-normal-dynamic-linear-model-definition-model-classes-and-the-superposition/02_polynomial-trend-models.en.txt 8.0 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/02_4-metropolis-hastings/03_random-walk-example-part-1.en.txt 7.9 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/02_bayesian-inference-in-the-ndlm-part-i/08_rcode-smoothing-in-the-ndlm-example_instructions.html 7.9 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/02_stationarity-the-acf-and-the-pacf/05_acf-pacf-differencing-and-smoothing-examples.en.txt 7.9 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/02_4-metropolis-hastings/02_demonstration.en.txt 7.7 kBmixture-models/04_applications-of-mixture-models/03_classification/03_classification-example.en.txt 7.7 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/02_bayesian-inference-in-the-ndlm-part-i/07_smoothing-in-the-ndlm-example.en.srt 7.6 kBbayesian-statistics/03_priors-and-models-for-discrete-data/03_bernoulli-binomial-data/03_lesson-7-3-posterior-mean-and-effective-sample-size.en.srt 7.6 kBmixture-models/01_basic-concepts-on-mixture-models/03_definition-of-mixture-models/02_mixtures-of-gaussians.en.txt 7.5 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/02_10-poisson-regression/03_predictive-distributions.en.txt 7.5 kBmixture-models/05_practical-considerations/02_determining-the-number-of-components-in-a-mixture-model/07_sample-code-for-estimating-the-number-of-components-and-the-partition-structure_instructions.html 7.5 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/03_11-hierarchical-modeling/06_linear-regression-example.en.srt 7.4 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/03_jags/02_model-writing-running-and-post-processing.en.txt 7.4 kBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/02_bayesian-inference-in-the-ar-p/08_spectral-representation-of-the-ar-p-example.en.srt 7.4 kBbayesian-statistics-time-series-analysis/04_week-4-normal-dynamic-linear-models-part-ii/02_bayesian-inference-in-the-ndlm-part-ii/01_filtering-smoothing-and-forecasting-unknown-observational-variance.en.txt 7.4 kBmixture-models/01_basic-concepts-on-mixture-models/04_likelihood-function-for-mixture-models/02_sampling-from-a-mixture-model.en.srt 7.3 kBmixture-models/04_applications-of-mixture-models/01_density-estimation/02_density-estimation-example.en.txt 7.3 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/03_11-hierarchical-modeling/07_linear-regression-example-in-jags.en.txt 7.3 kBbayesian-statistics/02_statistical-inference/02_frequentist-inference/03_lesson-4-2-likelihood-function-and-maximum-likelihood.en.srt 7.3 kBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/01_module-overview/01_course-introduction.en.srt 7.2 kBbayesian-statistics/01_probability-and-bayes-theorem/01_module-overview/01_course-introduction.en.srt 7.1 kBmixture-models/05_practical-considerations/02_determining-the-number-of-components-in-a-mixture-model/01_bayesian-information-criteria-bic.en.txt 7.0 kBmcmc-bayesian-statistics/03_common-statistical-models/02_7-linear-regression/03_jags-model-linear-regression.en.txt 6.8 kBmixture-models/05_practical-considerations/02_determining-the-number-of-components-in-a-mixture-model/04_estimating-the-number-of-components-in-bayesian-settings.en.txt 6.7 kBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/04_3-monte-carlo-estimation/02_monte-carlo-error-and-marginalization.en.srt 6.7 kBbayesian-statistics-capstone/02_model-selection-criteria/01_week-2/01_aic-and-bic-in-selecting-the-order-of-ar-process.en.txt 6.6 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/03_11-hierarchical-modeling/03_prior-predictive-simulation.en.txt 6.6 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/04_5-gibbs-sampling/02_conditionally-conjugate-prior-example-with-normal-likelihood.en.txt 6.6 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/01_introduction/01_welcome-to-bayesian-statistics-time-series.en.txt 6.6 kBmcmc-bayesian-statistics/03_common-statistical-models/02_7-linear-regression/05_alternative-models.en.txt 6.6 kBmixture-models/01_basic-concepts-on-mixture-models/04_likelihood-function-for-mixture-models/05_parameter-identifiability.en.txt 6.5 kBbayesian-statistics/01_probability-and-bayes-theorem/03_bayes-theorem/02_lesson-2-2-bayes-theorem.en.srt 6.5 kBmixture-models/01_basic-concepts-on-mixture-models/02_the-r-environment-for-statistical-computing/01_installing-and-using-r.en.srt 6.4 kBbayesian-statistics-capstone/03_bayesian-location-mixture-of-ar-p-model/01_week-3/03_coding-the-gibbs-sampler.en.txt 6.4 kBmcmc-bayesian-statistics/03_common-statistical-models/03_8-anova/01_introduction-to-anova.en.txt 6.4 kBbayesian-statistics/04_models-for-continuous-data/04_alternative-priors/01_lesson-11-1-non-informative-priors.en.txt 6.4 kBbayesian-statistics/01_probability-and-bayes-theorem/04_review-of-distributions/01_lesson-3-1-bernoulli-and-binomial-distributions.en.srt 6.3 kBbayesian-statistics/03_priors-and-models-for-discrete-data/04_poisson-data/01_lesson-8-1-poisson-data.en.txt 6.3 kBmcmc-bayesian-statistics/03_common-statistical-models/04_9-logistic-regression/01_introduction-to-logistic-regression.en.srt 6.2 kBbayesian-statistics-capstone/02_model-selection-criteria/01_week-2/03_deviance-information-criterion-dic.en.srt 6.2 kBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/02_bayesian-inference-in-the-ar-p/07_spectral-representation-of-the-ar-p.en.srt 6.1 kBbayesian-statistics/03_priors-and-models-for-discrete-data/02_priors/02_lesson-6-2-prior-predictive-binomial-example.en.srt 6.1 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/02_stationarity-the-acf-and-the-pacf/01_stationarity.en.srt 6.1 kBbayesian-statistics/02_statistical-inference/02_frequentist-inference/02_lesson-4-1-confidence-intervals.en.srt 6.1 kBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/03_2-bayesian-modeling/03_posterior-derivation.en.txt 6.0 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/02_4-metropolis-hastings/01_algorithm.en.txt 6.0 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/04_5-gibbs-sampling/01_multiple-parameter-sampling-and-full-conditional-distributions.en.txt 6.0 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/03_the-ar-1-process-definition-and-properties/03_simulating-from-an-ar-1-process.en.txt 6.0 kBmixture-models/03_bayesian-estimation-for-mixture-models/01_markov-chain-monte-carlo-algorithms-for-mixture-models/08_sample-code-for-mcmc-example-2_instructions.html 5.9 kBmcmc-bayesian-statistics/03_common-statistical-models/02_7-linear-regression/02_setup-in-r.en.txt 5.9 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/05_6-assessing-convergence/03_multiple-chains-burn-in-gelman-rubin-diagnostic.en.txt 5.9 kBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/02_1-statistical-modeling/02_modeling-process.en.txt 5.9 kBbayesian-statistics-capstone/01_bayesian-conjugate-analysis-for-autogressive-time-series-models/01_week-1/04_model-formulation.en.txt 5.9 kBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/03_2-bayesian-modeling/01_components-of-bayesian-models.en.txt 5.8 kBmixture-models/02_maximum-likelihood-estimation-for-mixture-models/01_the-em-algorithm-for-mixture-models/06_sample-code-for-em-example-2_instructions.html 5.8 kBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/04_3-monte-carlo-estimation/01_monte-carlo-integration.en.txt 5.8 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/02_bayesian-inference-in-the-ndlm-part-i/09_second-order-polynomial-filtering-and-smoothing-example.en.txt 5.7 kBmixture-models/04_applications-of-mixture-models/01_density-estimation/03_sample-code-for-density-estimation-problems_instructions.html 5.7 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/02_bayesian-inference-in-the-ndlm-part-i/10_using-the-dlm-package-in-r.en.txt 5.7 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/04_the-ar-1-maximum-likelihood-estimation-and-bayesian-inference/04_bayesian-inference-in-the-ar-1.en.txt 5.7 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/03_11-hierarchical-modeling/01_correlated-data.en.txt 5.6 kBmixture-models/03_bayesian-estimation-for-mixture-models/01_markov-chain-monte-carlo-algorithms-for-mixture-models/06_sample-code-for-mcmc-example-1_instructions.html 5.5 kBbayesian-statistics/03_priors-and-models-for-discrete-data/03_bernoulli-binomial-data/02_lesson-7-2-conjugate-priors.en.srt 5.5 kBbayesian-statistics/02_statistical-inference/02_frequentist-inference/07_introduction-to-r.en.txt 5.4 kBbayesian-statistics-capstone/03_bayesian-location-mixture-of-ar-p-model/01_week-3/05_determine-the-number-of-components_instructions.html 5.4 kBbayesian-statistics/02_statistical-inference/03_bayesian-inference/03_lesson-5-2-inference-example-bayesian.en.txt 5.3 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/03_11-hierarchical-modeling/05_posterior-predictive-simulation.en.txt 5.3 kBbayesian-statistics/02_statistical-inference/03_bayesian-inference/05_lesson-5-4-posterior-intervals.en.txt 5.3 kBbayesian-statistics/01_probability-and-bayes-theorem/04_review-of-distributions/02_lesson-3-2-uniform-distribution.en.srt 5.2 kBbayesian-statistics/02_statistical-inference/02_frequentist-inference/08_plotting-the-likelihood-in-r.en.srt 5.2 kBbayesian-statistics/01_probability-and-bayes-theorem/02_probability/02_lesson-1-1-classical-and-frequentist-probability.en.txt 5.2 kBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/03_2-bayesian-modeling/04_non-conjugate-models.en.txt 5.2 kBmcmc-bayesian-statistics/03_common-statistical-models/02_7-linear-regression/01_introduction-to-linear-regression.en.txt 5.1 kBmixture-models/01_basic-concepts-on-mixture-models/01_introduction/01_welcome-to-bayesian-statistics-mixture-models.en.srt 5.1 kBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/02_bayesian-inference-in-the-ar-p/03_model-order-selection.en.txt 5.1 kBbayesian-statistics/03_priors-and-models-for-discrete-data/02_priors/03_lesson-6-3-posterior-predictive-distribution.en.srt 5.1 kBbayesian-statistics-capstone/03_bayesian-location-mixture-of-ar-p-model/01_week-3/01_prediction-for-location-mixture-of-ar-models.en.srt 5.1 kBbayesian-statistics-capstone/03_bayesian-location-mixture-of-ar-p-model/01_week-3/06_prediction-for-location-mixture-of-ar-model.en.srt 5.1 kBmixture-models/02_maximum-likelihood-estimation-for-mixture-models/01_the-em-algorithm-for-mixture-models/04_sample-code-for-em-example-1_instructions.html 5.0 kBbayesian-statistics/01_probability-and-bayes-theorem/03_bayes-theorem/01_lesson-2-1-conditional-probability.en.srt 5.0 kBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/03_2-bayesian-modeling/02_model-specification.en.txt 5.0 kBmixture-models/05_practical-considerations/02_determining-the-number-of-components-in-a-mixture-model/03_sample-code-bayesian-information-criteria_instructions.html 5.0 kBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/02_1-statistical-modeling/01_objectives.en.txt 5.0 kBbayesian-statistics/02_statistical-inference/03_bayesian-inference/02_lesson-5-1-inference-example-frequentist.en.srt 4.9 kBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/02_bayesian-inference-in-the-ar-p/01_bayesian-inference-in-the-ar-p-reference-prior-conditional-likelihood.en.txt 4.9 kBbayesian-statistics-capstone/01_bayesian-conjugate-analysis-for-autogressive-time-series-models/01_week-1/01_introduction.en.srt 4.8 kBbayesian-statistics/02_statistical-inference/02_frequentist-inference/05_lesson-4-4-computing-the-mle-examples.en.srt 4.8 kBmcmc-bayesian-statistics/03_common-statistical-models/02_7-linear-regression/06_deviance-information-criterion-dic.en.srt 4.8 kBbayesian-statistics/02_statistical-inference/03_bayesian-inference/04_lesson-5-3-continuous-version-of-bayes-theorem.en.srt 4.8 kBmixture-models/05_practical-considerations/01_computational-considerations-for-mixture-models/04_sample-code-to-illustrate-multimodality-issues-1_instructions.html 4.8 kBbayesian-statistics-capstone/01_bayesian-conjugate-analysis-for-autogressive-time-series-models/01_week-1/08_prediction-for-ar-models.en.txt 4.8 kBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/01_the-general-ar-p-process/03_acf-of-the-ar-p.en.txt 4.7 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/02_stationarity-the-acf-and-the-pacf/02_the-autocorrelation-function-acf.en.txt 4.7 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/02_bayesian-inference-in-the-ndlm-part-i/07_smoothing-in-the-ndlm-example.en.txt 4.7 kBbayesian-statistics/03_priors-and-models-for-discrete-data/02_priors/01_lesson-6-1-priors-and-prior-predictive-distributions.en.srt 4.7 kBmixture-models/04_applications-of-mixture-models/02_clustering/03_sample-em-algorithm-for-clustering-problems_instructions.html 4.6 kBbayesian-statistics/03_priors-and-models-for-discrete-data/03_bernoulli-binomial-data/03_lesson-7-3-posterior-mean-and-effective-sample-size.en.txt 4.6 kBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/01_module-overview/01_course-introduction.en.txt 4.5 kBbayesian-statistics/02_statistical-inference/02_frequentist-inference/03_lesson-4-2-likelihood-function-and-maximum-likelihood.en.txt 4.5 kBmixture-models/04_applications-of-mixture-models/03_classification/04_sample-em-algorithm-for-classification-problems_instructions.html 4.5 kBbayesian-statistics/01_probability-and-bayes-theorem/01_module-overview/01_course-introduction.en.txt 4.5 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/03_11-hierarchical-modeling/06_linear-regression-example.en.txt 4.5 kBbayesian-statistics/01_probability-and-bayes-theorem/02_probability/03_lesson-1-2-bayesian-probability-and-coherence.en.srt 4.5 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/01_the-normal-dynamic-linear-model-definition-model-classes-and-the-superposition/03_regression-models.en.txt 4.4 kBbayesian-statistics-capstone/03_bayesian-location-mixture-of-ar-p-model/01_week-3/04_sample-code-for-the-gibbs-sampler_instructions.html 4.4 kBbayesian-statistics-time-series-analysis/04_week-4-normal-dynamic-linear-models-part-ii/01_seasonal-ndlms/03_building-ndlms-with-multiple-components-examples.en.txt 4.3 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/04_the-ar-1-maximum-likelihood-estimation-and-bayesian-inference/03_r-code-mle-for-the-ar-1-examples_instructions.html 4.2 kBbayesian-statistics/04_models-for-continuous-data/03_normal-data/01_lesson-10-1-normal-likelihood-with-variance-known.en.srt 4.2 kBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/04_3-monte-carlo-estimation/02_monte-carlo-error-and-marginalization.en.txt 4.2 kBbayesian-statistics/04_models-for-continuous-data/02_exponential-data/01_lesson-9-1-exponential-data.en.srt 4.2 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/01_the-normal-dynamic-linear-model-definition-model-classes-and-the-superposition/05_the-superposition-principle.en.txt 4.2 kBbayesian-statistics/04_models-for-continuous-data/04_alternative-priors/02_lesson-11-2-jeffreys-prior.en.srt 4.2 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/03_jags/01_download-install-setup.en.srt 4.1 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/04_the-ar-1-maximum-likelihood-estimation-and-bayesian-inference/05_bayesian-inference-in-the-ar-1-conditional-likelihood-example.en.srt 4.1 kBbayesian-statistics/02_statistical-inference/02_frequentist-inference/09_plotting-the-likelihood-in-excel.en.srt 4.1 kBmixture-models/01_basic-concepts-on-mixture-models/04_likelihood-function-for-mixture-models/01_hierarchical-representations.en.txt 4.1 kBbayesian-statistics/01_probability-and-bayes-theorem/04_review-of-distributions/01_lesson-3-1-bernoulli-and-binomial-distributions.en.txt 3.9 kBbayesian-statistics-capstone/01_bayesian-conjugate-analysis-for-autogressive-time-series-models/01_week-1/06_posterior-distribution-derivation_instructions.html 3.9 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/02_10-poisson-regression/01_introduction-to-poisson-regression.en.srt 3.9 kBmixture-models/05_practical-considerations/01_computational-considerations-for-mixture-models/05_sample-code-to-illustrate-multimodality-issues-2_instructions.html 3.9 kBmixture-models/01_basic-concepts-on-mixture-models/04_likelihood-function-for-mixture-models/02_sampling-from-a-mixture-model.en.txt 3.9 kBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/02_bayesian-inference-in-the-ar-p/08_spectral-representation-of-the-ar-p-example.en.txt 3.8 kBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/01_the-general-ar-p-process/06_rcode-simulating-data-from-an-ar-p_instructions.html 3.8 kBbayesian-statistics/02_statistical-inference/02_frequentist-inference/02_lesson-4-1-confidence-intervals.en.txt 3.8 kBbayesian-statistics/03_priors-and-models-for-discrete-data/02_priors/02_lesson-6-2-prior-predictive-binomial-example.en.txt 3.7 kBmcmc-bayesian-statistics/03_common-statistical-models/04_9-logistic-regression/01_introduction-to-logistic-regression.en.txt 3.7 kBbayesian-statistics-capstone/02_model-selection-criteria/01_week-2/03_deviance-information-criterion-dic.en.txt 3.7 kBbayesian-statistics/01_probability-and-bayes-theorem/03_bayes-theorem/02_lesson-2-2-bayes-theorem.en.txt 3.7 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/02_stationarity-the-acf-and-the-pacf/01_stationarity.en.txt 3.7 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/04_mixture-models/01_mixture-model-introduction-data-and-code_mixture.csv 3.5 kBmixture-models/01_basic-concepts-on-mixture-models/02_the-r-environment-for-statistical-computing/01_installing-and-using-r.en.txt 3.5 kBbayesian-statistics/02_statistical-inference/02_frequentist-inference/04_lesson-4-3-computing-the-mle.en.srt 3.5 kBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/02_bayesian-inference-in-the-ar-p/06_rcode-model-order-selection_instructions.html 3.4 kBbayesian-statistics/02_statistical-inference/01_module-overview/01_module-2-objectives-assignments-and-supplementary-materials_instructions.html 3.4 kBbayesian-statistics-capstone/01_bayesian-conjugate-analysis-for-autogressive-time-series-models/01_week-1/05_review-useful-distributions_instructions.html 3.4 kBbayesian-statistics/03_priors-and-models-for-discrete-data/03_bernoulli-binomial-data/02_lesson-7-2-conjugate-priors.en.txt 3.4 kBbayesian-statistics/03_priors-and-models-for-discrete-data/03_bernoulli-binomial-data/06_r-and-excel-code-from-example-analysis_L7_binomial.Excel.txt 3.3 kBbayesian-statistics-capstone/01_bayesian-conjugate-analysis-for-autogressive-time-series-models/01_week-1/03_read-data_instructions.html 3.3 kBbayesian-statistics/04_models-for-continuous-data/03_normal-data/02_lesson-10-2-normal-likelihood-with-variance-unknown.en.srt 3.3 kBbayesian-statistics/03_priors-and-models-for-discrete-data/02_priors/03_lesson-6-3-posterior-predictive-distribution.en.txt 3.3 kBmixture-models/01_basic-concepts-on-mixture-models/01_introduction/01_welcome-to-bayesian-statistics-mixture-models.en.txt 3.3 kBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/02_bayesian-inference-in-the-ar-p/07_spectral-representation-of-the-ar-p.en.txt 3.3 kBbayesian-statistics/01_probability-and-bayes-theorem/04_review-of-distributions/02_lesson-3-2-uniform-distribution.en.txt 3.2 kBbayesian-statistics/02_statistical-inference/02_frequentist-inference/08_plotting-the-likelihood-in-r.en.txt 3.2 kBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/02_bayesian-inference-in-the-ar-p/05_rcode-bayesian-inference-ar-p-conditional-likelihood_instructions.html 3.1 kBbayesian-statistics/01_probability-and-bayes-theorem/03_bayes-theorem/01_lesson-2-1-conditional-probability.en.txt 3.1 kBbayesian-statistics-capstone/01_bayesian-conjugate-analysis-for-autogressive-time-series-models/01_week-1/01_introduction.en.txt 3.1 kBmixture-models/04_applications-of-mixture-models/03_classification/04_sample-em-algorithm-for-classification-problems_wine_test.txt 3.0 kBbayesian-statistics/02_statistical-inference/02_frequentist-inference/05_lesson-4-4-computing-the-mle-examples.en.txt 3.0 kBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/02_bayesian-inference-in-the-ar-p/09_rcode-spectral-density-of-ar-p_instructions.html 3.0 kBbayesian-statistics/02_statistical-inference/03_bayesian-inference/02_lesson-5-1-inference-example-frequentist.en.txt 3.0 kBbayesian-statistics/02_statistical-inference/03_bayesian-inference/04_lesson-5-3-continuous-version-of-bayes-theorem.en.txt 3.0 kBmcmc-bayesian-statistics/03_common-statistical-models/02_7-linear-regression/06_deviance-information-criterion-dic.en.txt 3.0 kBbayesian-statistics/03_priors-and-models-for-discrete-data/03_bernoulli-binomial-data/06_r-and-excel-code-from-example-analysis_L7_binomial.R.txt 2.9 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/01_module-overview/01_module-4-assignments-and-materials_instructions.html 2.9 kBbayesian-statistics/03_priors-and-models-for-discrete-data/02_priors/01_lesson-6-1-priors-and-prior-predictive-distributions.en.txt 2.9 kBbayesian-statistics/04_models-for-continuous-data/01_module-overview/01_module-4-objectives-assignments-and-supplementary-materials_instructions.html 2.9 kBbayesian-statistics/03_priors-and-models-for-discrete-data/03_bernoulli-binomial-data/01_lesson-7-1-bernoulli-binomial-likelihood-with-uniform-prior.en.srt 2.8 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/03_the-ar-1-process-definition-and-properties/04_r-code-sample-data-from-ar-1-processes_instructions.html 2.8 kBbayesian-statistics/01_probability-and-bayes-theorem/02_probability/03_lesson-1-2-bayesian-probability-and-coherence.en.txt 2.8 kBbayesian-statistics/01_probability-and-bayes-theorem/01_module-overview/02_module-1-objectives-assignments-and-supplementary-materials_instructions.html 2.7 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/04_the-ar-1-maximum-likelihood-estimation-and-bayesian-inference/06_r-code-ar-1-bayesian-inference-conditional-likelihood-example_instructions.html 2.7 kBbayesian-statistics/04_models-for-continuous-data/04_alternative-priors/02_lesson-11-2-jeffreys-prior.en.txt 2.7 kBbayesian-statistics-capstone/03_bayesian-location-mixture-of-ar-p-model/01_week-3/01_prediction-for-location-mixture-of-ar-models.en.txt 2.6 kBbayesian-statistics-capstone/03_bayesian-location-mixture-of-ar-p-model/01_week-3/06_prediction-for-location-mixture-of-ar-model.en.txt 2.6 kBbayesian-statistics/01_probability-and-bayes-theorem/04_review-of-distributions/03_lesson-3-3-exponential-and-normal-distributions.en.srt 2.6 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/01_module-overview/01_module-2-assignments-and-materials_instructions.html 2.6 kBbayesian-statistics/04_models-for-continuous-data/03_normal-data/01_lesson-10-1-normal-likelihood-with-variance-known.en.txt 2.6 kBbayesian-statistics/04_models-for-continuous-data/02_exponential-data/01_lesson-9-1-exponential-data.en.txt 2.6 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/03_jags/01_download-install-setup.en.txt 2.6 kBbayesian-statistics-capstone/01_bayesian-conjugate-analysis-for-autogressive-time-series-models/01_week-1/02_prerequisite-skill-checklist_instructions.html 2.5 kBbayesian-statistics/02_statistical-inference/02_frequentist-inference/09_plotting-the-likelihood-in-excel.en.txt 2.5 kBmcmc-bayesian-statistics/03_common-statistical-models/01_module-overview/01_module-3-assignments-and-materials_instructions.html 2.4 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/02_10-poisson-regression/01_introduction-to-poisson-regression.en.txt 2.4 kBbayesian-statistics/03_priors-and-models-for-discrete-data/01_module-overview/01_module-3-objectives-assignments-and-supplementary-materials_instructions.html 2.4 kBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/01_module-overview/02_module-1-assignments-and-materials_instructions.html 2.4 kBmixture-models/05_practical-considerations/01_computational-considerations-for-mixture-models/02_sample-code-to-illustrate-numerical-stability-issues_instructions.html 2.2 kBbayesian-statistics/02_statistical-inference/02_frequentist-inference/04_lesson-4-3-computing-the-mle.en.txt 2.2 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/04_the-ar-1-maximum-likelihood-estimation-and-bayesian-inference/05_bayesian-inference-in-the-ar-1-conditional-likelihood-example.en.txt 2.2 kBbayesian-statistics/04_models-for-continuous-data/05_linear-regression/05_r-and-excel-code-for-regression_L12_regression.R-V2.txt 2.1 kBbayesian-statistics/04_models-for-continuous-data/03_normal-data/02_lesson-10-2-normal-likelihood-with-variance-unknown.en.txt 2.1 kBbayesian-statistics-capstone/04_peer-reviewed-data-analysis-project/01_week-4/01_acknowledgments-and-reference_instructions.html 2.0 kBbayesian-statistics/04_models-for-continuous-data/05_linear-regression/05_r-and-excel-code-for-regression_L12_regression.Excel.2016-V2.txt 1.9 kBbayesian-statistics/04_models-for-continuous-data/05_linear-regression/05_r-and-excel-code-for-regression_L12_regression.Excel.StatPlus-V2.txt 1.9 kBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/02_bayesian-inference-in-the-ar-p/02_rcode-maximum-likelihood-estimation-ar-p-conditional-likelihood_instructions.html 1.8 kBbayesian-statistics/03_priors-and-models-for-discrete-data/03_bernoulli-binomial-data/01_lesson-7-1-bernoulli-binomial-likelihood-with-uniform-prior.en.txt 1.8 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/02_stationarity-the-acf-and-the-pacf/07_r-code-simulate-data-from-a-white-noise-process_instructions.html 1.7 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/01_introduction/03_list-of-references_instructions.html 1.7 kBmcmc-bayesian-statistics/05_capstone-project/01_course-conclusion/01_course-conclusion.en.srt 1.7 kBbayesian-statistics/01_probability-and-bayes-theorem/04_review-of-distributions/03_lesson-3-3-exponential-and-normal-distributions.en.txt 1.6 kBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/01_the-general-ar-p-process/05_rcode-computing-the-roots-of-the-ar-polynomial_instructions.html 1.6 kBmixture-models/01_basic-concepts-on-mixture-models/04_likelihood-function-for-mixture-models/03_sample-code-for-simulating-from-a-mixture-model_instructions.html 1.6 kBmixture-models/01_basic-concepts-on-mixture-models/03_definition-of-mixture-models/05_example-of-a-unimodal-symmetric-and-heavy-tailed-mixture-of-gaussians_instructions.html 1.6 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/02_stationarity-the-acf-and-the-pacf/06_r-code-differencing-and-filtering-via-moving-averages_instructions.html 1.5 kBbayesian-statistics/04_models-for-continuous-data/06_course-conclusion/01_conclusion.en.srt 1.5 kBmixture-models/01_basic-concepts-on-mixture-models/03_definition-of-mixture-models/08_example-of-a-zero-inflated-log-gaussian-distribution_instructions.html 1.5 kBmixture-models/01_basic-concepts-on-mixture-models/03_definition-of-mixture-models/07_example-of-a-zero-inflated-negative-binomial-distribution_instructions.html 1.4 kBbayesian-statistics/01_probability-and-bayes-theorem/04_review-of-distributions/04_supplementary-material-for-lesson-3_instructions.html 1.4 kBbayesian-statistics/04_models-for-continuous-data/03_normal-data/03_supplementary-material-for-lesson-10_instructions.html 1.3 kBbayesian-statistics/02_statistical-inference/03_bayesian-inference/01_background-for-lesson-5_instructions.html 1.3 kBbayesian-statistics/04_models-for-continuous-data/05_linear-regression/05_r-and-excel-code-for-regression_instructions.html 1.3 kBmixture-models/01_basic-concepts-on-mixture-models/02_the-r-environment-for-statistical-computing/02_an-introduction-to-r_instructions.html 1.3 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/03_11-hierarchical-modeling/02_normal-hierarchical-model_instructions.html 1.2 kBbayesian-statistics/04_models-for-continuous-data/05_linear-regression/01_background-for-lesson-12_instructions.html 1.2 kBmixture-models/01_basic-concepts-on-mixture-models/03_definition-of-mixture-models/04_example-of-a-unimodal-and-skewed-mixture-of-gaussians_instructions.html 1.2 kBbayesian-statistics/02_statistical-inference/02_frequentist-inference/01_background-for-lesson-4_instructions.html 1.2 kBbayesian-statistics/02_statistical-inference/03_bayesian-inference/06_supplementary-material-for-lesson-5_instructions.html 1.2 kBmixture-models/01_basic-concepts-on-mixture-models/03_definition-of-mixture-models/03_example-of-a-bimodal-mixture-of-gaussians_instructions.html 1.2 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/01_introduction/02_introduction-to-r_instructions.html 1.2 kBbayesian-statistics/02_statistical-inference/02_frequentist-inference/06_supplementary-material-for-lesson-4_instructions.html 1.2 kBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/05_background-for-lesson-4/01_markov-chains_instructions.html 1.2 kBbayesian-statistics/01_probability-and-bayes-theorem/03_bayes-theorem/03_supplementary-material-for-lesson-2_instructions.html 1.2 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/02_10-poisson-regression/04_prior-sensitivity-analysis_instructions.html 1.2 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/05_6-assessing-convergence/02_autocorrelation_instructions.html 1.2 kBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/03_2-bayesian-modeling/05_reference-common-probability-distributions_instructions.html 1.2 kBmcmc-bayesian-statistics/05_capstone-project/01_course-conclusion/02_further-reading-and-acknowledgements_instructions.html 1.2 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/03_11-hierarchical-modeling/08_applications-of-hierarchical-modeling_instructions.html 1.2 kBbayesian-statistics/03_priors-and-models-for-discrete-data/03_bernoulli-binomial-data/06_r-and-excel-code-from-example-analysis_instructions.html 1.2 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/04_mixture-models/01_mixture-model-introduction-data-and-code_instructions.html 1.2 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/03_11-hierarchical-modeling/09_code-and-data-for-lesson-11_instructions.html 1.1 kBbayesian-statistics/04_models-for-continuous-data/04_alternative-priors/03_supplementary-material-for-lesson-11_instructions.html 1.1 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/03_jags/03_alternative-mcmc-software_instructions.html 1.1 kBbayesian-statistics/01_probability-and-bayes-theorem/02_probability/01_background-for-lesson-1_instructions.html 1.1 kBmixture-models/06_Resources/01_notes-on-finite-mixture-models/01__resources.html 1.1 kBmcmc-bayesian-statistics/05_capstone-project/01_course-conclusion/01_course-conclusion.en.txt 1.1 kBbayesian-statistics-time-series-analysis/04_week-4-normal-dynamic-linear-models-part-ii/02_bayesian-inference-in-the-ndlm-part-ii/02_summary-of-filtering-smoothing-and-forecasting-distributions-ndlm-unknown_instructions.html 1.1 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/04_the-ar-1-maximum-likelihood-estimation-and-bayesian-inference/07_bayesian-inference-in-the-ar-1-full-likelihood-example_instructions.html 1.1 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/01_the-normal-dynamic-linear-model-definition-model-classes-and-the-superposition/04_summary-of-polynomial-trend-and-regression-models_instructions.html 1.1 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/02_stationarity-the-acf-and-the-pacf/04_differencing-and-smoothing_instructions.html 1.1 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/04_the-ar-1-maximum-likelihood-estimation-and-bayesian-inference/01_review-of-maximum-likelihood-and-bayesian-inference-in-regression_instructions.html 1.1 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/02_bayesian-inference-in-the-ndlm-part-i/06_summary-of-the-smoothing-and-forecasting-distributions_instructions.html 1.1 kBbayesian-statistics-time-series-analysis/04_week-4-normal-dynamic-linear-models-part-ii/01_seasonal-ndlms/02_fourier-representation-example-1_instructions.html 1.1 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/01_the-normal-dynamic-linear-model-definition-model-classes-and-the-superposition/06_superposition-principle-general-case_instructions.html 1.1 kBbayesian-statistics-capstone/01_bayesian-conjugate-analysis-for-autogressive-time-series-models/01_week-1/10_extended-ar-model_instructions.html 1.1 kBbayesian-statistics-time-series-analysis/04_week-4-normal-dynamic-linear-models-part-ii/01_seasonal-ndlms/04_summary-dlm-fourier-representation_instructions.html 1.1 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/03_the-ar-1-process-definition-and-properties/02_the-pacf-of-the-ar-1-process_instructions.html 1.1 kBmcmc-bayesian-statistics/03_common-statistical-models/05_multiple-factor-anova/01_multiple-factor-anova_instructions.html 1.1 kBbayesian-statistics-time-series-analysis/03_week-3-normal-dynamic-linear-models-part-i/02_bayesian-inference-in-the-ndlm-part-i/02_summary-of-the-filtering-distributions_instructions.html 1.1 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/05_6-assessing-convergence/04_code-for-lesson-6_instructions.html 1.1 kBmcmc-bayesian-statistics/03_common-statistical-models/02_7-linear-regression/07_code-for-lesson-7_instructions.html 1.1 kBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/01_the-general-ar-p-process/07_the-ar-p-review_instructions.html 1.1 kBbayesian-statistics-time-series-analysis/02_week-2-the-ar-p-process/02_bayesian-inference-in-the-ar-p/10_arima-processes_instructions.html 1.0 kBbayesian-statistics-time-series-analysis/01_week-1-introduction-to-time-series-and-the-ar-1-process/02_stationarity-the-acf-and-the-pacf/03_the-partial-autocorrelation-function-pacf_instructions.html 1.0 kBmcmc-bayesian-statistics/01_statistical-modeling-and-monte-carlo-estimation/04_3-monte-carlo-estimation/05_code-for-lesson-3_instructions.html 1.0 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/02_4-metropolis-hastings/05_code-for-lesson-4_instructions.html 1.0 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/03_jags/04_code-from-jags-introduction_instructions.html 1.0 kBmcmc-bayesian-statistics/02_markov-chain-monte-carlo-mcmc/04_5-gibbs-sampling/04_code-for-lesson-5_instructions.html 1.0 kBmcmc-bayesian-statistics/03_common-statistical-models/03_8-anova/03_code-for-lesson-8_instructions.html 1.0 kBmcmc-bayesian-statistics/03_common-statistical-models/04_9-logistic-regression/04_code-for-lesson-9_instructions.html 1.0 kBmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/02_10-poisson-regression/05_code-for-lesson-10_instructions.html 1.0 kBbayesian-statistics/04_models-for-continuous-data/06_course-conclusion/01_conclusion.en.txt 994 Bytesbayesian-statistics-capstone/01_bayesian-conjugate-analysis-for-autogressive-time-series-models/01_week-1/03_read-data_earthquakes.txt 912 Bytesbayesian-statistics-capstone/01_bayesian-conjugate-analysis-for-autogressive-time-series-models/01_week-1/03_read-data_GoogleSearchIndex.txt 852 Bytesmcmc-bayesian-statistics/04_count-data-and-hierarchical-modeling/03_11-hierarchical-modeling/09_code-and-data-for-lesson-11_cookies.dat 687 Bytes
0. Websites you may like/[CourserHub.com].url 123 BytesReadme.txt 51 Bytes Statistics Data Analysis in SPSS Descriptive Statistics 554.0 MB Udemy - Introductory statistics Part1 Descriptive Statistics 556.3 MB Introductory statistics Part1 Descriptive Statistics 534.9 MB Statistics Data Analysis in SPSS Descriptive Statistics.tgz 554.0 MB
Lynda - Excel 2016 Statistics Essential Training Part 2... 245.5 MB Statistics for People Who (Think They) Hate Statistics -... 129.5 MB [ FreeCourseWeb.com ] Udemy - Statistics in Mathematics ... 931.0 MB Andy Field Discovering Statistics Using IBM SPSS... 99.9 MB Statistics 9th ed - Robert S. Witte, John S. Witte... 167.3 MB
IBM.SPSS Statistics v24 windows 64 bits 923.0 MB
Andor [HDTV 720p][Cap.205] 8145次下载
Cap
HDTV
720p
Маня и Груня.2024.WEB-DL 720p.Files-x 5109次下载
2024
WEB
DL
Havoc.2025.1080p.NF.WEB-DL.DDP5.1.Atmos.H.264-EniaHD.mkv 5077次下载
WEB
DL
DDP5
The.Super.Cube.S01E09.1080p.iQ.WEB-DL.AAC2.0.H.264-VARYG.mkv 5040次下载
WEB
DL
Cube
Star.Wars.Andor.S02E05.I.Have.Friends.Everywhere.1080p.DS... 4944次下载
WEB
DL
Star
新桥恋人.电影港 地址发布页 www.dygang.me 收藏不迷路 4365次下载
me
www
发布页
Night at the Museum Battle of the Smithsonian 2009 2160p... 4161次下载
KiNGDOM
WEB
DL
www.UIndex.org - ... 4126次下载
www
MeGusta
HEVC
Palma.2.2024.WEB-DL.1080p.ExKinoRay.mkv 4000次下载
WEB
DL
2.2024
Guns N' Roses - Greatest Hits Live Broadcast Collection... 3915次下载
Live
Remastered
Hits
Andor [HDTV 720p][Cap.205] 8145次下载
Cap
HDTV
720p
(同人CG集) [白色絵の具 (無味ムスミ)] 友達の母が巨乳で金髪の淫乱美女でした.zip 6346次下载
zip
巨乳
CG
You.S05E02.720p.HEVC.x265-MeGusta[EZTVx.to].mkv 6221次下载
S05E02
HEVC
720p
up-mod-minecraft-play-with-friends-v1-21-80-28-... 6023次下载
982108028
mod
play
the.last.of.us.s02e02.1080p.web.h264-successful... 5335次下载
web
last
h264
异世降临.6v电影 地址发布页 www.6v123.net 收藏不迷路 5220次下载
www
发布页
6v123
Opasnaya.blizost.S01.2025.WEB-DL.1080p 5127次下载
WEB
DL
S01
Маня и Груня.2024.WEB-DL 720p.Files-x 5109次下载
2024
WEB
DL
Andor.S02E02.Sagrona.Teema.1080p.HEVC.x265-MeGu... 5100次下载
HEVC
Andor
mkv
Havoc.2025.1080p.NF.WEB-DL.DDP5.1.Atmos.H.264-E... 5077次下载
WEB
DL
DDP5
Shoot.Paragon.1970.DVDRip.XviD-AFO [NO-RAR] -...
Shoot
www
AFO
DASS-041-U
DASS
041
www.UIndex.org - Andy Richter Controls...
www
Episode
S01E04
[AniDub]_Sora_Kara_Furu_Ichioku_no_Hoshi_[Oriko_Qbiq]
Furu
no
Oriko
[BEST-TORRENTS.COM]...
Cheese
DL
2160p
【成人抖音-黑料-换妻-直播-手机搜7t7a.cc】麻豆传媒映画・爆操黑丝车模小姐姐・超级圆润...
麻豆
小姐姐
车模
Por siempre [HDTV 720p][Cap.106]
siempre
720p
Cap
Workaholics.S04E01.Season.4.Episode.1.WEBRip.72...
Workaholics
Episode
HoC
MobLand.S01E01.1080P.ENG.ITA.H264-TheBlackKing.mkv
ENG
H264
ITA
43606101 - Public Dick Flash Compilation..mp4
Dick
..
Compilation
吃瓜！去欧美留学的林淑瑶 ️被欧美大鸡巴驯化3P大战被洋吊深喉狂艹！
欧美
3P
被洋
Hellraiser III Hell On Earth 1992 1080p BluRay...
5.1
1992
AAC
Ghosts S04E21 Kyle 1080p PMTP WEB-DL DDP5 1 H...
WEB
DL
STC
[Gecko] Araiguma Calcal-dan - S01E07...
WEB
DL
AAC
salamander-2-salamander-deluxe-pack-play-statio...
salamander
play
full
Catalina Cruz live cam show pack
Catalina
show
live
SDMU-534
534
SDMU
dccdom.com@MIDV111C
com
MIDV111C
dccdom