课程目录

• 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