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Readme.txt

/.../01_bayesian-conjugate-analysis-for-autogressive-time-series-models/01_week-1/

01_introduction.en.srt

01_introduction.en.txt

01_introduction.mp4

02_prerequisite-skill-checklist_instructions.html

03_read-data_GoogleSearchIndex.txt

03_read-data_earthquakes.txt

03_read-data_instructions.html

04_model-formulation.en.srt

04_model-formulation.en.txt

04_model-formulation.mp4

05_review-useful-distributions_instructions.html

06_posterior-distribution-derivation_instructions.html

07_ar-model-fitting-example_instructions.html

08_prediction-for-ar-models.en.srt

08_prediction-for-ar-models.en.txt

08_prediction-for-ar-models.mp4

09_ar-model-prediction-example_instructions.html

10_extended-ar-model_Honor-Extend_AR_model.pdf

10_extended-ar-model_instructions.html

/.../02_model-selection-criteria/01_week-2/

01_aic-and-bic-in-selecting-the-order-of-ar-process.en.srt

01_aic-and-bic-in-selecting-the-order-of-ar-process.en.txt

01_aic-and-bic-in-selecting-the-order-of-ar-process.mp4

02_aic-and-bic-example_instructions.html

03_deviance-information-criterion-dic.en.srt

03_deviance-information-criterion-dic.en.txt

03_deviance-information-criterion-dic.mp4

04_dic-example_instructions.html

/.../03_bayesian-location-mixture-of-ar-p-model/01_week-3/

01_prediction-for-location-mixture-of-ar-models.en.srt

01_prediction-for-location-mixture-of-ar-models.en.txt

01_prediction-for-location-mixture-of-ar-models.mp4

02_full-conditional-distributions-of-model-parameters.en.srt

02_full-conditional-distributions-of-model-parameters.en.txt

02_full-conditional-distributions-of-model-parameters.mp4

03_coding-the-gibbs-sampler.en.srt

03_coding-the-gibbs-sampler.en.txt

03_coding-the-gibbs-sampler.mp4

04_sample-code-for-the-gibbs-sampler_instructions.html

05_determine-the-number-of-components_instructions.html

06_prediction-for-location-mixture-of-ar-model.en.srt

06_prediction-for-location-mixture-of-ar-model.en.txt

06_prediction-for-location-mixture-of-ar-model.mp4

07_location-and-scale-mixture-of-ar-model_instructions.html

/.../04_peer-reviewed-data-analysis-project/01_week-4/

01_acknowledgments-and-reference_instructions.html

/.../01_week-1-introduction-to-time-series-and-the-ar-1-process/01_introduction/

01_welcome-to-bayesian-statistics-time-series.en.srt

01_welcome-to-bayesian-statistics-time-series.en.txt

01_welcome-to-bayesian-statistics-time-series.mp4

02_introduction-to-r_R-intro.pdf

02_introduction-to-r_instructions.html

03_list-of-references_instructions.html

/.../02_stationarity-the-acf-and-the-pacf/

01_stationarity.en.srt

01_stationarity.en.txt

01_stationarity.mp4

02_the-autocorrelation-function-acf.en.srt

02_the-autocorrelation-function-acf.en.txt

02_the-autocorrelation-function-acf.mp4

03_the-partial-autocorrelation-function-pacf_instructions.html

03_the-partial-autocorrelation-function-pacf_pacf.pdf

04_differencing-and-smoothing_differencing_and_smoothing.pdf

04_differencing-and-smoothing_instructions.html

05_acf-pacf-differencing-and-smoothing-examples.en.srt

05_acf-pacf-differencing-and-smoothing-examples.en.txt

05_acf-pacf-differencing-and-smoothing-examples.mp4

06_r-code-differencing-and-filtering-via-moving-averages_instructions.html

07_r-code-simulate-data-from-a-white-noise-process_instructions.html

/.../03_the-ar-1-process-definition-and-properties/

01_the-ar-1.en.srt

01_the-ar-1.en.txt

01_the-ar-1.mp4

02_the-pacf-of-the-ar-1-process_instructions.html

02_the-pacf-of-the-ar-1-process_pacf_ar1.pdf

03_simulating-from-an-ar-1-process.en.srt

03_simulating-from-an-ar-1-process.en.txt

03_simulating-from-an-ar-1-process.mp4

04_r-code-sample-data-from-ar-1-processes_instructions.html

/.../04_the-ar-1-maximum-likelihood-estimation-and-bayesian-inference/

01_review-of-maximum-likelihood-and-bayesian-inference-in-regression_Bayesian_regression_review.pdf

01_review-of-maximum-likelihood-and-bayesian-inference-in-regression_instructions.html

02_maximum-likelihood-estimation-in-the-ar-1.en.srt

02_maximum-likelihood-estimation-in-the-ar-1.en.txt

02_maximum-likelihood-estimation-in-the-ar-1.mp4

03_r-code-mle-for-the-ar-1-examples_instructions.html

04_bayesian-inference-in-the-ar-1.en.srt

04_bayesian-inference-in-the-ar-1.en.txt

04_bayesian-inference-in-the-ar-1.mp4

05_bayesian-inference-in-the-ar-1-conditional-likelihood-example.en.srt

05_bayesian-inference-in-the-ar-1-conditional-likelihood-example.en.txt

05_bayesian-inference-in-the-ar-1-conditional-likelihood-example.mp4

06_r-code-ar-1-bayesian-inference-conditional-likelihood-example_instructions.html

07_bayesian-inference-in-the-ar-1-full-likelihood-example_bayesian_ar1_full_likelihood_example.pdf

07_bayesian-inference-in-the-ar-1-full-likelihood-example_instructions.html

/.../01_the-general-ar-p-process/

01_definition-and-state-space-representation.en.srt

01_definition-and-state-space-representation.en.txt

01_definition-and-state-space-representation.mp4

02_examples.en.srt

02_examples.en.txt

02_examples.mp4

03_acf-of-the-ar-p.en.srt

03_acf-of-the-ar-p.en.txt

03_acf-of-the-ar-p.mp4

04_simulating-data-from-an-ar-p.en.srt

04_simulating-data-from-an-ar-p.en.txt

04_simulating-data-from-an-ar-p.mp4

05_rcode-computing-the-roots-of-the-ar-polynomial_instructions.html

06_rcode-simulating-data-from-an-ar-p_instructions.html

07_the-ar-p-review_instructions.html

07_the-ar-p-review_summary.pdf

/.../02_bayesian-inference-in-the-ar-p/

01_bayesian-inference-in-the-ar-p-reference-prior-conditional-likelihood.en.srt

01_bayesian-inference-in-the-ar-p-reference-prior-conditional-likelihood.en.txt

01_bayesian-inference-in-the-ar-p-reference-prior-conditional-likelihood.mp4

02_rcode-maximum-likelihood-estimation-ar-p-conditional-likelihood_instructions.html

03_model-order-selection.en.srt

03_model-order-selection.en.txt

03_model-order-selection.mp4

04_example-bayesian-inference-in-the-ar-p-conditional-likelihood.en.srt

04_example-bayesian-inference-in-the-ar-p-conditional-likelihood.en.txt

04_example-bayesian-inference-in-the-ar-p-conditional-likelihood.mp4

05_rcode-bayesian-inference-ar-p-conditional-likelihood_instructions.html

06_rcode-model-order-selection_instructions.html

07_spectral-representation-of-the-ar-p.en.srt

07_spectral-representation-of-the-ar-p.en.txt

07_spectral-representation-of-the-ar-p.mp4

08_spectral-representation-of-the-ar-p-example.en.srt

08_spectral-representation-of-the-ar-p-example.en.txt

08_spectral-representation-of-the-ar-p-example.mp4

09_rcode-spectral-density-of-ar-p_instructions.html

10_arima-processes_ARIMA.pdf

10_arima-processes_instructions.html

/.../01_the-normal-dynamic-linear-model-definition-model-classes-and-the-superposition/

01_ndlm-definition.en.srt

01_ndlm-definition.en.txt

01_ndlm-definition.mp4

02_polynomial-trend-models.en.srt

02_polynomial-trend-models.en.txt

02_polynomial-trend-models.mp4

03_regression-models.en.srt

03_regression-models.en.txt

03_regression-models.mp4

04_summary-of-polynomial-trend-and-regression-models_instructions.html

04_summary-of-polynomial-trend-and-regression-models_polynomial_regression_ndlms.pdf

05_the-superposition-principle.en.srt

05_the-superposition-principle.en.txt

05_the-superposition-principle.mp4

06_superposition-principle-general-case_instructions.html

06_superposition-principle-general-case_superposition_principle.pdf

/.../02_bayesian-inference-in-the-ndlm-part-i/

01_filtering.en.srt

01_filtering.en.txt

01_filtering.mp4

02_summary-of-the-filtering-distributions_instructions.html

02_summary-of-the-filtering-distributions_ndlm_filtering.pdf

03_filtering-in-the-ndlm-example.en.srt

03_filtering-in-the-ndlm-example.en.txt

03_filtering-in-the-ndlm-example.mp4

04_rcode-filtering-in-the-ndlm-example_instructions.html

05_smoothing-and-forecasting.en.srt

05_smoothing-and-forecasting.en.txt

05_smoothing-and-forecasting.mp4

06_summary-of-the-smoothing-and-forecasting-distributions_instructions.html

06_summary-of-the-smoothing-and-forecasting-distributions_ndlm_smoothing_forecasting.pdf

07_smoothing-in-the-ndlm-example.en.srt

07_smoothing-in-the-ndlm-example.en.txt

07_smoothing-in-the-ndlm-example.mp4

08_rcode-smoothing-in-the-ndlm-example_instructions.html

09_second-order-polynomial-filtering-and-smoothing-example.en.srt

09_second-order-polynomial-filtering-and-smoothing-example.en.txt

09_second-order-polynomial-filtering-and-smoothing-example.mp4

10_using-the-dlm-package-in-r.en.srt

10_using-the-dlm-package-in-r.en.txt

10_using-the-dlm-package-in-r.mp4

11_rcode-using-the-dlm-package-in-r_instructions.html

/.../01_seasonal-ndlms/

01_fourier-representation.en.srt

01_fourier-representation.en.txt

01_fourier-representation.mp4

02_fourier-representation-example-1_instructions.html

02_fourier-representation-example-1_seasonal_models_example1.pdf

03_building-ndlms-with-multiple-components-examples.en.srt

03_building-ndlms-with-multiple-components-examples.en.txt

03_building-ndlms-with-multiple-components-examples.mp4

04_summary-dlm-fourier-representation_instructions.html

04_summary-dlm-fourier-representation_seasonal_fourier_dlm.pdf

/.../02_bayesian-inference-in-the-ndlm-part-ii/

01_filtering-smoothing-and-forecasting-unknown-observational-variance.en.srt

01_filtering-smoothing-and-forecasting-unknown-observational-variance.en.txt

01_filtering-smoothing-and-forecasting-unknown-observational-variance.mp4

02_summary-of-filtering-smoothing-and-forecasting-distributions-ndlm-unknown_instructions.html

02_summary-of-filtering-smoothing-and-forecasting-distributions-ndlm-unknown_ndlm_filtering_smoothing_forecasting_unknown_v.pdf

03_specifying-the-system-covariance-matrix-via-discount-factors.en.srt

03_specifying-the-system-covariance-matrix-via-discount-factors.en.txt

03_specifying-the-system-covariance-matrix-via-discount-factors.mp4

04_ndlm-unknown-observational-variance-example.en.srt

04_ndlm-unknown-observational-variance-example.en.txt

04_ndlm-unknown-observational-variance-example.mp4

05_rcode-ndlm-unknown-observational-variance-example_instructions.html

/.../03_case-studies/

01_eeg-data.en.srt

01_eeg-data.en.txt

01_eeg-data.mp4

02_google-trends.en.srt

02_google-trends.en.txt

02_google-trends.mp4

/.../01_module-overview/

01_course-introduction.en.srt

01_course-introduction.en.txt

01_course-introduction.mp4

02_module-1-objectives-assignments-and-supplementary-materials_instructions.html

/.../01_probability-and-bayes-theorem/02_probability/

01_background-for-lesson-1_L1_background.pdf

01_background-for-lesson-1_instructions.html

02_lesson-1-1-classical-and-frequentist-probability.en.srt

02_lesson-1-1-classical-and-frequentist-probability.en.txt

02_lesson-1-1-classical-and-frequentist-probability.mp4

03_lesson-1-2-bayesian-probability-and-coherence.en.srt

03_lesson-1-2-bayesian-probability-and-coherence.en.txt

03_lesson-1-2-bayesian-probability-and-coherence.mp4

/.../03_bayes-theorem/

01_lesson-2-1-conditional-probability.en.srt

01_lesson-2-1-conditional-probability.en.txt

01_lesson-2-1-conditional-probability.mp4

02_lesson-2-2-bayes-theorem.en.srt

02_lesson-2-2-bayes-theorem.en.txt

02_lesson-2-2-bayes-theorem.mp4

03_supplementary-material-for-lesson-2_L2_supp-v2.pdf

03_supplementary-material-for-lesson-2_instructions.html

/.../04_review-of-distributions/

01_lesson-3-1-bernoulli-and-binomial-distributions.en.srt

01_lesson-3-1-bernoulli-and-binomial-distributions.en.txt

01_lesson-3-1-bernoulli-and-binomial-distributions.mp4

02_lesson-3-2-uniform-distribution.en.srt

02_lesson-3-2-uniform-distribution.en.txt

02_lesson-3-2-uniform-distribution.mp4

03_lesson-3-3-exponential-and-normal-distributions.en.srt

03_lesson-3-3-exponential-and-normal-distributions.en.txt

03_lesson-3-3-exponential-and-normal-distributions.mp4

04_supplementary-material-for-lesson-3_L3_supp.pdf

04_supplementary-material-for-lesson-3_instructions.html

/.../01_module-overview/

01_module-2-objectives-assignments-and-supplementary-materials_instructions.html

/.../02_frequentist-inference/

01_background-for-lesson-4_L4_background.pdf

01_background-for-lesson-4_instructions.html

02_lesson-4-1-confidence-intervals.en.srt

02_lesson-4-1-confidence-intervals.en.txt

02_lesson-4-1-confidence-intervals.mp4

03_lesson-4-2-likelihood-function-and-maximum-likelihood.en.srt

03_lesson-4-2-likelihood-function-and-maximum-likelihood.en.txt

03_lesson-4-2-likelihood-function-and-maximum-likelihood.mp4

04_lesson-4-3-computing-the-mle.en.srt

04_lesson-4-3-computing-the-mle.en.txt

04_lesson-4-3-computing-the-mle.mp4

05_lesson-4-4-computing-the-mle-examples.en.srt

05_lesson-4-4-computing-the-mle-examples.en.txt

05_lesson-4-4-computing-the-mle-examples.mp4

06_supplementary-material-for-lesson-4_L4_supp-v2.pdf

06_supplementary-material-for-lesson-4_instructions.html

07_introduction-to-r.en.srt

07_introduction-to-r.en.txt

07_introduction-to-r.mp4

08_plotting-the-likelihood-in-r.en.srt

08_plotting-the-likelihood-in-r.en.txt

08_plotting-the-likelihood-in-r.mp4

09_plotting-the-likelihood-in-excel.en.srt

09_plotting-the-likelihood-in-excel.en.txt

09_plotting-the-likelihood-in-excel.mp4

/.../03_bayesian-inference/

01_background-for-lesson-5_L5_background.pdf

01_background-for-lesson-5_instructions.html

02_lesson-5-1-inference-example-frequentist.en.srt

02_lesson-5-1-inference-example-frequentist.en.txt

02_lesson-5-1-inference-example-frequentist.mp4

03_lesson-5-2-inference-example-bayesian.en.srt

03_lesson-5-2-inference-example-bayesian.en.txt

03_lesson-5-2-inference-example-bayesian.mp4

04_lesson-5-3-continuous-version-of-bayes-theorem.en.srt

04_lesson-5-3-continuous-version-of-bayes-theorem.en.txt

04_lesson-5-3-continuous-version-of-bayes-theorem.mp4

05_lesson-5-4-posterior-intervals.en.srt

05_lesson-5-4-posterior-intervals.en.txt

05_lesson-5-4-posterior-intervals.mp4

06_supplementary-material-for-lesson-5_L5_supp.pdf

06_supplementary-material-for-lesson-5_instructions.html

/.../01_module-overview/

01_module-3-objectives-assignments-and-supplementary-materials_instructions.html

/.../03_priors-and-models-for-discrete-data/02_priors/

01_lesson-6-1-priors-and-prior-predictive-distributions.en.srt

01_lesson-6-1-priors-and-prior-predictive-distributions.en.txt

01_lesson-6-1-priors-and-prior-predictive-distributions.mp4

02_lesson-6-2-prior-predictive-binomial-example.en.srt

02_lesson-6-2-prior-predictive-binomial-example.en.txt

02_lesson-6-2-prior-predictive-binomial-example.mp4

03_lesson-6-3-posterior-predictive-distribution.en.srt

03_lesson-6-3-posterior-predictive-distribution.en.txt

03_lesson-6-3-posterior-predictive-distribution.mp4

/.../03_bernoulli-binomial-data/

01_lesson-7-1-bernoulli-binomial-likelihood-with-uniform-prior.en.srt

01_lesson-7-1-bernoulli-binomial-likelihood-with-uniform-prior.en.txt

01_lesson-7-1-bernoulli-binomial-likelihood-with-uniform-prior.mp4

02_lesson-7-2-conjugate-priors.en.srt

02_lesson-7-2-conjugate-priors.en.txt

02_lesson-7-2-conjugate-priors.mp4

03_lesson-7-3-posterior-mean-and-effective-sample-size.en.srt

03_lesson-7-3-posterior-mean-and-effective-sample-size.en.txt

03_lesson-7-3-posterior-mean-and-effective-sample-size.mp4

04_data-analysis-example-in-r.en.srt

04_data-analysis-example-in-r.en.txt

04_data-analysis-example-in-r.mp4

05_data-analysis-example-in-excel.en.srt

05_data-analysis-example-in-excel.en.txt

05_data-analysis-example-in-excel.mp4

06_r-and-excel-code-from-example-analysis_L7_binomial.Excel.txt

06_r-and-excel-code-from-example-analysis_L7_binomial.R.txt

06_r-and-excel-code-from-example-analysis_instructions.html

/.../04_poisson-data/

01_lesson-8-1-poisson-data.en.srt

01_lesson-8-1-poisson-data.en.txt

01_lesson-8-1-poisson-data.mp4

/.../01_module-overview/

01_module-4-objectives-assignments-and-supplementary-materials_instructions.html

/.../02_exponential-data/

01_lesson-9-1-exponential-data.en.srt

01_lesson-9-1-exponential-data.en.txt

01_lesson-9-1-exponential-data.mp4

/.../03_normal-data/

01_lesson-10-1-normal-likelihood-with-variance-known.en.srt

01_lesson-10-1-normal-likelihood-with-variance-known.en.txt

01_lesson-10-1-normal-likelihood-with-variance-known.mp4

02_lesson-10-2-normal-likelihood-with-variance-unknown.en.srt

02_lesson-10-2-normal-likelihood-with-variance-unknown.en.txt

02_lesson-10-2-normal-likelihood-with-variance-unknown.mp4

03_supplementary-material-for-lesson-10_L10_supp.pdf

03_supplementary-material-for-lesson-10_instructions.html

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01_lesson-11-1-non-informative-priors.en.srt

01_lesson-11-1-non-informative-priors.en.txt

01_lesson-11-1-non-informative-priors.mp4

02_lesson-11-2-jeffreys-prior.en.srt

02_lesson-11-2-jeffreys-prior.en.txt

02_lesson-11-2-jeffreys-prior.mp4

03_supplementary-material-for-lesson-11_L11_supp.pdf

03_supplementary-material-for-lesson-11_instructions.html

/.../05_linear-regression/

01_background-for-lesson-12_L12_background.pdf

01_background-for-lesson-12_instructions.html

02_linear-regression-in-r.en.srt

02_linear-regression-in-r.en.txt

02_linear-regression-in-r.mp4

03_linear-regression-in-excel-analysis-toolpak.en.srt

03_linear-regression-in-excel-analysis-toolpak.en.txt

03_linear-regression-in-excel-analysis-toolpak.mp4

04_linear-regression-in-excel-statplus-by-analystsoft.en.srt

04_linear-regression-in-excel-statplus-by-analystsoft.en.txt

04_linear-regression-in-excel-statplus-by-analystsoft.mp4

05_r-and-excel-code-for-regression_L12_regression.Excel.2016-V2.txt

05_r-and-excel-code-for-regression_L12_regression.Excel.StatPlus-V2.txt

05_r-and-excel-code-for-regression_L12_regression.R-V2.txt

05_r-and-excel-code-for-regression_instructions.html

/.../06_course-conclusion/

01_conclusion.en.srt

01_conclusion.en.txt

01_conclusion.mp4

/.../01_module-overview/

01_course-introduction.en.srt

01_course-introduction.en.txt

01_course-introduction.mp4

02_module-1-assignments-and-materials_instructions.html

/.../02_1-statistical-modeling/

01_objectives.en.srt

01_objectives.en.txt

01_objectives.mp4

02_modeling-process.en.srt

02_modeling-process.en.txt

02_modeling-process.mp4

/.../03_2-bayesian-modeling/

01_components-of-bayesian-models.en.srt

01_components-of-bayesian-models.en.txt

01_components-of-bayesian-models.mp4

02_model-specification.en.srt

02_model-specification.en.txt

02_model-specification.mp4

03_posterior-derivation.en.srt

03_posterior-derivation.en.txt

03_posterior-derivation.mp4

04_non-conjugate-models.en.srt

04_non-conjugate-models.en.txt

04_non-conjugate-models.mp4

05_reference-common-probability-distributions_Distributions.pdf

05_reference-common-probability-distributions_instructions.html

/.../04_3-monte-carlo-estimation/

01_monte-carlo-integration.en.srt

01_monte-carlo-integration.en.txt

01_monte-carlo-integration.mp4

02_monte-carlo-error-and-marginalization.en.srt

02_monte-carlo-error-and-marginalization.en.txt

02_monte-carlo-error-and-marginalization.mp4

03_computing-examples.en.srt

03_computing-examples.en.txt

03_computing-examples.mp4

04_computing-monte-carlo-error.en.srt

04_computing-monte-carlo-error.en.txt

04_computing-monte-carlo-error.mp4

05_code-for-lesson-3_instructions.html

05_code-for-lesson-3_lesson_03.html

/.../05_background-for-lesson-4/

01_markov-chains_background_MarkovChains.html

01_markov-chains_instructions.html

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03_alternative-mcmc-software_MCMCsoftware.pdf

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