【资源目录】:
├──1-bayesian-statistics
| ├──01_probability-and-bayes-theorem
| | ├──01_module-overview
| | ├──02_probability
| | ├──03_bayes-theorem
| | └──04_review-of-distributions
| ├──02_statistical-inference
| | ├──01_module-overview
| | ├──02_frequentist-inference
| | └──03_bayesian-inference
| ├──03_priors-and-models-for-discrete-data
| | ├──01_module-overview
| | ├──02_priors
| | ├──03_bernoulli-binomial-data
| | └──04_poisson-data
| └──04_models-for-continuous-data
| | ├──01_module-overview
| | ├──02_exponential-data
| | ├──03_normal-data
| | ├──04_alternative-priors
| | ├──05_linear-regression
| | └──06_course-conclusion
├──2-mcmc-bayesian-statistics
| ├──01_statistical-modeling-and-monte-carlo-estimation
| | ├──01_module-overview
| | ├──02_1-statistical-modeling
| | ├──03_2-bayesian-modeling
| | ├──04_3-monte-carlo-estimation
| | └──05_background-for-lesson-4
| ├──02_markov-chain-monte-carlo-mcmc
| | ├──01_module-overview
| | ├──02_4-metropolis-hastings
| | ├──03_jags
| | ├──04_5-gibbs-sampling
| | └──05_6-assessing-convergence
| ├──03_common-statistical-models
| | ├──01_module-overview
| | ├──02_7-linear-regression
| | ├──03_8-anova
| | ├──04_9-logistic-regression
| | └──05_multiple-factor-anova
| ├──04_count-data-and-hierarchical-modeling
| | ├──01_module-overview
| | ├──02_10-poisson-regression
| | ├──03_11-hierarchical-modeling
| | └──04_mixture-models
| └──05_capstone-project
| | └──01_course-conclusion
├──3-mixture-models
| ├──01_basic-concepts-on-mixture-models
| | ├──01_introduction
| | ├──02_the-r-environment-for-statistical-computing
| | ├──03_definition-of-mixture-models
| | └──04_likelihood-function-for-mixture-models
| ├──02_maximum-likelihood-estimation-for-mixture-models
| | └──01_the-em-algorithm-for-mixture-models
| ├──03_bayesian-estimation-for-mixture-models
| | └──01_markov-chain-monte-carlo-algorithms-for-mixture-models
| ├──04_applications-of-mixture-models
| | ├──01_density-estimation
| | ├──02_clustering
| | └──03_classification
| ├──05_practical-considerations
| | ├──01_computational-considerations-for-mixture-models
| | └──02_determining-the-number-of-components-in-a-mixture-model
| └──06_Resources
| | └──01_notes-on-finite-mixture-models
├──4-bayesian-statistics-time-series-analysis
| ├──01_week-1-introduction-to-time-series-and-the-ar-1-process
| | ├──01_introduction
| | ├──02_stationarity-the-acf-and-the-pacf
| | ├──03_the-ar-1-process-definition-and-properties
| | └──04_the-ar-1-maximum-likelihood-estimation-and-bayesian-inference
| ├──02_week-2-the-ar-p-process
| | ├──01_the-general-ar-p-process
| | └──02_bayesian-inference-in-the-ar-p
| ├──03_week-3-normal-dynamic-linear-models-part-i
| | ├──01_the-normal-dynamic-linear-model-definition-model-classes-and-the-superposition
| | └──02_bayesian-inference-in-the-ndlm-part-i
| └──04_week-4-normal-dynamic-linear-models-part-ii
| | ├──01_seasonal-ndlms
| | ├──02_bayesian-inference-in-the-ndlm-part-ii
| | └──03_case-studies
└──5-bayesian-statistics-capstone
| ├──01_bayesian-conjugate-analysis-for-autogressive-time-series-models
| | └──01_week-1
| ├──02_model-selection-criteria
| | └──01_week-2
| ├──03_bayesian-location-mixture-of-ar-p-model
| | └──01_week-3
| └──04_peer-reviewed-data-analysis-project
| | └──01_week-4
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