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Coursera Bayesian Statistics Specialization

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Files:
  1. [CourserHub.com].url 123 bytes
  2. Readme.txt 51 bytes
  3. 01_introduction.en.srt 4.7 KB
  4. 01_introduction.en.txt 3.0 KB
  5. 01_introduction.mp4 17.8 MB
  6. 02_prerequisite-skill-checklist_instructions.html 2.5 KB
  7. 03_read-data_GoogleSearchIndex.txt 852 bytes
  8. 03_read-data_earthquakes.txt 912 bytes
  9. 03_read-data_instructions.html 3.2 KB
  10. 04_model-formulation.en.srt 10.0 KB
  11. 04_model-formulation.en.txt 5.7 KB
  12. 04_model-formulation.mp4 59.9 MB
  13. 05_review-useful-distributions_instructions.html 3.3 KB
  14. 06_posterior-distribution-derivation_instructions.html 3.8 KB
  15. 07_ar-model-fitting-example_instructions.html 648.0 KB
  16. 08_prediction-for-ar-models.en.srt 7.9 KB
  17. 08_prediction-for-ar-models.en.txt 4.7 KB
  18. 08_prediction-for-ar-models.mp4 33.5 MB
  19. 09_ar-model-prediction-example_instructions.html 60.4 KB
  20. 10_extended-ar-model_Honor-Extend_AR_model.pdf 151.0 KB
  21. 10_extended-ar-model_instructions.html 1.0 KB
  22. 01_aic-and-bic-in-selecting-the-order-of-ar-process.en.srt 12.6 KB
  23. 01_aic-and-bic-in-selecting-the-order-of-ar-process.en.txt 6.5 KB
  24. 01_aic-and-bic-in-selecting-the-order-of-ar-process.mp4 40.7 MB
  25. 02_aic-and-bic-example_instructions.html 114.5 KB
  26. 03_deviance-information-criterion-dic.en.srt 6.1 KB
  27. 03_deviance-information-criterion-dic.en.txt 3.6 KB
  28. 03_deviance-information-criterion-dic.mp4 20.3 MB
  29. 04_dic-example_instructions.html 102.9 KB
  30. 01_prediction-for-location-mixture-of-ar-models.en.srt 5.0 KB
  31. 01_prediction-for-location-mixture-of-ar-models.en.txt 2.6 KB
  32. 01_prediction-for-location-mixture-of-ar-models.mp4 13.9 MB
  33. 02_full-conditional-distributions-of-model-parameters.en.srt 18.0 KB
  34. 02_full-conditional-distributions-of-model-parameters.en.txt 9.1 KB
  35. 02_full-conditional-distributions-of-model-parameters.mp4 102.7 MB
  36. 03_coding-the-gibbs-sampler.en.srt 10.2 KB
  37. 03_coding-the-gibbs-sampler.en.txt 6.3 KB
  38. 03_coding-the-gibbs-sampler.mp4 24.8 MB
  39. 04_sample-code-for-the-gibbs-sampler_instructions.html 4.3 KB
  40. 05_determine-the-number-of-components_instructions.html 5.3 KB
  41. 06_prediction-for-location-mixture-of-ar-model.en.srt 5.0 KB
  42. 06_prediction-for-location-mixture-of-ar-model.en.txt 2.6 KB
  43. 06_prediction-for-location-mixture-of-ar-model.mp4 13.9 MB
  44. 07_location-and-scale-mixture-of-ar-model_instructions.html 267.7 KB
  45. 01_acknowledgments-and-reference_instructions.html 2.0 KB
  46. 01_welcome-to-bayesian-statistics-time-series.en.srt 12.1 KB
  47. 01_welcome-to-bayesian-statistics-time-series.en.txt 6.4 KB
  48. 01_welcome-to-bayesian-statistics-time-series.mp4 27.5 MB
  49. 02_introduction-to-r_R-intro.pdf 612.3 KB
  50. 02_introduction-to-r_instructions.html 1.2 KB
  51. 03_list-of-references_instructions.html 1.6 KB
  52. 01_stationarity.en.srt 6.0 KB
  53. 01_stationarity.en.txt 3.6 KB
  54. 01_stationarity.mp4 28.1 MB
  55. 02_the-autocorrelation-function-acf.en.srt 8.8 KB
  56. 02_the-autocorrelation-function-acf.en.txt 4.6 KB
  57. 02_the-autocorrelation-function-acf.mp4 31.6 MB
  58. 03_the-partial-autocorrelation-function-pacf_instructions.html 1.0 KB
  59. 03_the-partial-autocorrelation-function-pacf_pacf.pdf 73.1 KB
  60. 04_differencing-and-smoothing_differencing_and_smoothing.pdf 81.6 KB
  61. 04_differencing-and-smoothing_instructions.html 1.0 KB
  62. 05_acf-pacf-differencing-and-smoothing-examples.en.srt 14.8 KB
  63. 05_acf-pacf-differencing-and-smoothing-examples.en.txt 7.7 KB
  64. 05_acf-pacf-differencing-and-smoothing-examples.mp4 28.9 MB
  65. 06_r-code-differencing-and-filtering-via-moving-averages_instructions.html 1.5 KB
  66. 07_r-code-simulate-data-from-a-white-noise-process_instructions.html 1.7 KB
  67. 01_the-ar-1.en.srt 16.8 KB
  68. 01_the-ar-1.en.txt 8.6 KB
  69. 01_the-ar-1.mp4 58.0 MB
  70. 02_the-pacf-of-the-ar-1-process_instructions.html 1.0 KB
  71. 02_the-pacf-of-the-ar-1-process_pacf_ar1.pdf 57.9 KB
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  76. 01_review-of-maximum-likelihood-and-bayesian-inference-in-regression_Bayesian_regression_review.pdf 90.2 KB
  77. 01_review-of-maximum-likelihood-and-bayesian-inference-in-regression_instructions.html 1.0 KB
  78. 02_maximum-likelihood-estimation-in-the-ar-1.en.srt 18.8 KB
  79. 02_maximum-likelihood-estimation-in-the-ar-1.en.txt 10.9 KB
  80. 02_maximum-likelihood-estimation-in-the-ar-1.mp4 121.3 MB
  81. 03_r-code-mle-for-the-ar-1-examples_instructions.html 4.1 KB
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  85. 05_bayesian-inference-in-the-ar-1-conditional-likelihood-example.en.srt 4.0 KB
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  87. 05_bayesian-inference-in-the-ar-1-conditional-likelihood-example.mp4 8.4 MB
  88. 06_r-code-ar-1-bayesian-inference-conditional-likelihood-example_instructions.html 2.6 KB
  89. 07_bayesian-inference-in-the-ar-1-full-likelihood-example_bayesian_ar1_full_likelihood_example.pdf 84.6 KB
  90. 07_bayesian-inference-in-the-ar-1-full-likelihood-example_instructions.html 1.1 KB
  91. 01_definition-and-state-space-representation.en.srt 23.5 KB
  92. 01_definition-and-state-space-representation.en.txt 12.0 KB
  93. 01_definition-and-state-space-representation.mp4 79.8 MB
  94. 02_examples.en.srt 18.6 KB
  95. 02_examples.en.txt 9.7 KB
  96. 02_examples.mp4 68.9 MB
  97. 03_acf-of-the-ar-p.en.srt 8.8 KB
  98. 03_acf-of-the-ar-p.en.txt 4.6 KB
  99. 03_acf-of-the-ar-p.mp4 29.1 MB
  100. 04_simulating-data-from-an-ar-p.en.srt 16.5 KB
  101. 04_simulating-data-from-an-ar-p.en.txt 8.5 KB
  102. 04_simulating-data-from-an-ar-p.mp4 30.6 MB
  103. 05_rcode-computing-the-roots-of-the-ar-polynomial_instructions.html 1.6 KB
  104. 06_rcode-simulating-data-from-an-ar-p_instructions.html 3.7 KB
  105. 07_the-ar-p-review_instructions.html 1.0 KB
  106. 07_the-ar-p-review_summary.pdf 113.7 KB
  107. 01_bayesian-inference-in-the-ar-p-reference-prior-conditional-likelihood.en.srt 7.9 KB
  108. 01_bayesian-inference-in-the-ar-p-reference-prior-conditional-likelihood.en.txt 4.8 KB
  109. 01_bayesian-inference-in-the-ar-p-reference-prior-conditional-likelihood.mp4 33.1 MB
  110. 02_rcode-maximum-likelihood-estimation-ar-p-conditional-likelihood_instructions.html 1.8 KB
  111. 03_model-order-selection.en.srt 9.6 KB
  112. 03_model-order-selection.en.txt 5.0 KB
  113. 03_model-order-selection.mp4 24.4 MB
  114. 04_example-bayesian-inference-in-the-ar-p-conditional-likelihood.en.srt 16.1 KB
  115. 04_example-bayesian-inference-in-the-ar-p-conditional-likelihood.en.txt 8.3 KB
  116. 04_example-bayesian-inference-in-the-ar-p-conditional-likelihood.mp4 34.7 MB
  117. 05_rcode-bayesian-inference-ar-p-conditional-likelihood_instructions.html 3.1 KB
  118. 06_rcode-model-order-selection_instructions.html 3.4 KB
  119. 07_spectral-representation-of-the-ar-p.en.srt 6.0 KB
  120. 07_spectral-representation-of-the-ar-p.en.txt 3.2 KB
  121. 07_spectral-representation-of-the-ar-p.mp4 17.9 MB
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  123. 08_spectral-representation-of-the-ar-p-example.en.txt 3.7 KB
  124. 08_spectral-representation-of-the-ar-p-example.mp4 13.7 MB
  125. 09_rcode-spectral-density-of-ar-p_instructions.html 2.9 KB
  126. 10_arima-processes_ARIMA.pdf 81.8 KB
  127. 10_arima-processes_instructions.html 1.0 KB
  128. 01_ndlm-definition.en.srt 18.4 KB
  129. 01_ndlm-definition.en.txt 9.7 KB
  130. 01_ndlm-definition.mp4 61.1 MB
  131. 02_polynomial-trend-models.en.srt 12.8 KB
  132. 02_polynomial-trend-models.en.txt 7.8 KB
  133. 02_polynomial-trend-models.mp4 49.2 MB
  134. 03_regression-models.en.srt 8.5 KB
  135. 03_regression-models.en.txt 4.3 KB
  136. 03_regression-models.mp4 29.1 MB
  137. 04_summary-of-polynomial-trend-and-regression-models_instructions.html 1.0 KB
  138. 04_summary-of-polynomial-trend-and-regression-models_polynomial_regression_ndlms.pdf 88.0 KB
  139. 05_the-superposition-principle.en.srt 7.9 KB
  140. 05_the-superposition-principle.en.txt 4.1 KB
  141. 05_the-superposition-principle.mp4 23.0 MB
  142. 06_superposition-principle-general-case_instructions.html 1.0 KB
  143. 06_superposition-principle-general-case_superposition_principle.pdf 71.0 KB
  144. 01_filtering.en.srt 17.9 KB
  145. 01_filtering.en.txt 10.4 KB
  146. 01_filtering.mp4 70.5 MB
  147. 02_summary-of-the-filtering-distributions_instructions.html 1.0 KB
  148. 02_summary-of-the-filtering-distributions_ndlm_filtering.pdf 80.3 KB
  149. 03_filtering-in-the-ndlm-example.en.srt 19.6 KB
  150. 03_filtering-in-the-ndlm-example.en.txt 10.1 KB
  151. 03_filtering-in-the-ndlm-example.mp4 39.1 MB
  152. 04_rcode-filtering-in-the-ndlm-example_instructions.html 8.2 KB
  153. 05_smoothing-and-forecasting.en.srt 13.0 KB
  154. 05_smoothing-and-forecasting.en.txt 7.8 KB
  155. 05_smoothing-and-forecasting.mp4 18.7 MB
  156. 06_summary-of-the-smoothing-and-forecasting-distributions_instructions.html 1.0 KB
  157. 06_summary-of-the-smoothing-and-forecasting-distributions_ndlm_smoothing_forecasting.pdf 71.2 KB
  158. 07_smoothing-in-the-ndlm-example.en.srt 7.4 KB
  159. 07_smoothing-in-the-ndlm-example.en.txt 4.6 KB
  160. 07_smoothing-in-the-ndlm-example.mp4 18.7 MB
  161. 08_rcode-smoothing-in-the-ndlm-example_instructions.html 7.7 KB
  162. 09_second-order-polynomial-filtering-and-smoothing-example.en.srt 8.8 KB
  163. 09_second-order-polynomial-filtering-and-smoothing-example.en.txt 5.6 KB
  164. 09_second-order-polynomial-filtering-and-smoothing-example.mp4 20.0 MB
  165. 10_using-the-dlm-package-in-r.en.srt 9.2 KB
  166. 10_using-the-dlm-package-in-r.en.txt 5.5 KB
  167. 10_using-the-dlm-package-in-r.mp4 23.5 MB
  168. 11_rcode-using-the-dlm-package-in-r_instructions.html 8.8 KB
  169. 01_fourier-representation.en.srt 18.2 KB
  170. 01_fourier-representation.en.txt 9.5 KB
  171. 01_fourier-representation.mp4 63.0 MB
  172. 02_fourier-representation-example-1_instructions.html 1.0 KB
  173. 02_fourier-representation-example-1_seasonal_models_example1.pdf 72.3 KB
  174. 03_building-ndlms-with-multiple-components-examples.en.srt 8.3 KB
  175. 03_building-ndlms-with-multiple-components-examples.en.txt 4.2 KB
  176. 03_building-ndlms-with-multiple-components-examples.mp4 34.8 MB
  177. 04_summary-dlm-fourier-representation_instructions.html 1.0 KB
  178. 04_summary-dlm-fourier-representation_seasonal_fourier_dlm.pdf 92.0 KB
  179. 01_filtering-smoothing-and-forecasting-unknown-observational-variance.en.srt 11.6 KB
  180. 01_filtering-smoothing-and-forecasting-unknown-observational-variance.en.txt 7.2 KB
  181. 01_filtering-smoothing-and-forecasting-unknown-observational-variance.mp4 17.3 MB
  182. 02_summary-of-filtering-smoothing-and-forecasting-distributions-ndlm-unknown_instructions.html 1.1 KB
  183. 02_summary-of-filtering-smoothing-and-forecasting-distributions-ndlm-unknown_ndlm_filtering_smoothing_forecasting_unknown_v.pdf 105.1 KB
  184. 03_specifying-the-system-covariance-matrix-via-discount-factors.en.srt 17.9 KB
  185. 03_specifying-the-system-covariance-matrix-via-discount-factors.en.txt 10.6 KB
  186. 03_specifying-the-system-covariance-matrix-via-discount-factors.mp4 76.5 MB
  187. 04_ndlm-unknown-observational-variance-example.en.srt 18.3 KB
  188. 04_ndlm-unknown-observational-variance-example.en.txt 9.5 KB
  189. 04_ndlm-unknown-observational-variance-example.mp4 35.5 MB
  190. 05_rcode-ndlm-unknown-observational-variance-example_instructions.html 11.0 KB
  191. 01_eeg-data.en.srt 22.1 KB
  192. 01_eeg-data.en.txt 11.5 KB
  193. 01_eeg-data.mp4 47.0 MB
  194. 02_google-trends.en.srt 18.7 KB
  195. 02_google-trends.en.txt 11.8 KB
  196. 02_google-trends.mp4 46.3 MB
  197. 01_course-introduction.en.srt 6.9 KB
  198. 01_course-introduction.en.txt 4.4 KB
  199. 01_course-introduction.mp4 13.6 MB
  200. 02_module-1-objectives-assignments-and-supplementary-materials_instructions.html 2.7 KB
  201. 01_background-for-lesson-1_L1_background.pdf 84.4 KB
  202. 01_background-for-lesson-1_instructions.html 1.1 KB
  203. 02_lesson-1-1-classical-and-frequentist-probability.en.srt 8.0 KB
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  205. 02_lesson-1-1-classical-and-frequentist-probability.mp4 32.3 MB
  206. 03_lesson-1-2-bayesian-probability-and-coherence.en.srt 4.4 KB
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  209. 01_lesson-2-1-conditional-probability.en.srt 4.9 KB
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  211. 01_lesson-2-1-conditional-probability.mp4 27.5 MB
  212. 02_lesson-2-2-bayes-theorem.en.srt 6.3 KB
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  214. 02_lesson-2-2-bayes-theorem.mp4 35.5 MB
  215. 03_supplementary-material-for-lesson-2_L2_supp-v2.pdf 29.2 KB
  216. 03_supplementary-material-for-lesson-2_instructions.html 1.2 KB
  217. 01_lesson-3-1-bernoulli-and-binomial-distributions.en.srt 6.2 KB
  218. 01_lesson-3-1-bernoulli-and-binomial-distributions.en.txt 3.8 KB
  219. 01_lesson-3-1-bernoulli-and-binomial-distributions.mp4 30.0 MB
  220. 02_lesson-3-2-uniform-distribution.en.srt 5.1 KB
  221. 02_lesson-3-2-uniform-distribution.en.txt 3.1 KB
  222. 02_lesson-3-2-uniform-distribution.mp4 30.1 MB
  223. 03_lesson-3-3-exponential-and-normal-distributions.en.srt 2.6 KB
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  225. 03_lesson-3-3-exponential-and-normal-distributions.mp4 17.1 MB
  226. 04_supplementary-material-for-lesson-3_L3_supp.pdf 152.8 KB
  227. 04_supplementary-material-for-lesson-3_instructions.html 1.4 KB
  228. 01_module-2-objectives-assignments-and-supplementary-materials_instructions.html 3.4 KB
  229. 01_background-for-lesson-4_L4_background.pdf 100.5 KB
  230. 01_background-for-lesson-4_instructions.html 1.2 KB
  231. 02_lesson-4-1-confidence-intervals.en.srt 6.0 KB
  232. 02_lesson-4-1-confidence-intervals.en.txt 3.7 KB
  233. 02_lesson-4-1-confidence-intervals.mp4 28.2 MB
  234. 03_lesson-4-2-likelihood-function-and-maximum-likelihood.en.srt 7.1 KB
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  237. 04_lesson-4-3-computing-the-mle.en.srt 3.4 KB
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  239. 04_lesson-4-3-computing-the-mle.mp4 17.4 MB
  240. 05_lesson-4-4-computing-the-mle-examples.en.srt 4.7 KB
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  242. 05_lesson-4-4-computing-the-mle-examples.mp4 25.4 MB
  243. 06_supplementary-material-for-lesson-4_L4_supp-v2.pdf 105.0 KB
  244. 06_supplementary-material-for-lesson-4_instructions.html 1.2 KB
  245. 07_introduction-to-r.en.srt 8.7 KB
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  247. 07_introduction-to-r.mp4 13.1 MB
  248. 08_plotting-the-likelihood-in-r.en.srt 5.0 KB
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  253. 09_plotting-the-likelihood-in-excel.mp4 8.9 MB
  254. 01_background-for-lesson-5_L5_background.pdf 127.0 KB
  255. 01_background-for-lesson-5_instructions.html 1.3 KB
  256. 02_lesson-5-1-inference-example-frequentist.en.srt 4.8 KB
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  587. 01_density-estimation-using-mixture-models.en.txt 8.5 KB
  588. 01_density-estimation-using-mixture-models.mp4 58.2 MB
  589. 02_density-estimation-example.en.srt 13.7 KB
  590. 02_density-estimation-example.en.txt 7.1 KB
  591. 02_density-estimation-example.mp4 20.6 MB
  592. 03_sample-code-for-density-estimation-problems_instructions.html 5.6 KB
  593. 01_mixture-models-for-clustering.en.srt 29.9 KB
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  600. 01_mixture-models-and-naive-bayes-classifiers.en.srt 23.6 KB
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  606. 03_classification-example.en.srt 11.9 KB
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  610. 04_sample-em-algorithm-for-classification-problems_wine_test.txt 3.0 KB
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  624. 02_bayesian-information-criteria-example.en.srt 15.4 KB
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  626. 02_bayesian-information-criteria-example.mp4 18.6 MB
  627. 03_sample-code-bayesian-information-criteria_instructions.html 4.9 KB
  628. 04_estimating-the-number-of-components-in-bayesian-settings.en.srt 10.6 KB
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  633. 05_estimating-the-full-partition-structure-in-bayesian-settings.mp4 101.9 MB
  634. 06_example-bayesian-inference-for-the-partition-structure.en.srt 23.9 KB
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  636. 06_example-bayesian-inference-for-the-partition-structure.mp4 28.8 MB
  637. 07_sample-code-for-estimating-the-number-of-components-and-the-partition-structure_instructions.html 7.3 KB
  638. 01__mixturemodels.pdf 1.4 MB
  639. 01__resources.html 1.1 KB

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