Course: Bayesian Computational Statistics
Modules:
Course Overview, Instructor Introduction, Module 1 Introduction, Bayes' rule and its consequences Pt. 1, Bayes' rule and its consequences Pt. 2, Fundamentals of Bayesian inference Pt. 1, Fundamentals of Bayesian inference Pt. 2, Fundamentals of Bayesian inference Pt. 3, Fundamentals of Bayesian inference Pt. 4, Bayesian Computation Pt. 1, Bayesian Computation Pt. 2
Module 2 Introduction, Binomial and Posterior Distributions Pt. 1, Binomial and Posterior Distributions Pt. 2, Binomial and Posterior Distributions Pt. 3, Binomial and Posterior Distributions Pt. 4, Binomial and Posterior Distributions Pt. 5, Binomial and Posterior Distributions Pt. 6, Priors Pt. 1, Priors Pt. 2, Priors Pt. 3, Other Single-Parameter Models Pt. 1, Other Single-Parameter Models Pt. 2, Other Single-Parameter Models Pt. 3, Other Single-Parameter Models Pt. 4, Other Single-Parameter Models Pt. 5, Other Single-Parameter Models Pt. 6, Other Single-Parameter Models Pt. 7
Module 3 Introduction, Nuisance Parameters Pt. 1, Nuisance Parameters Pt. 2, Nuisance Parameters Pt. 3, Nuisance Parameters Pt. 4, Nuisance Parameters Pt. 5, Nuisance Parameters Pt. 6, Nuisance Parameters Pt. 7, Conjugate Priors Pt. 1, Conjugate Priors Pt. 2, Conjugate Priors Pt. 3, More Models and Applications Pt. 1, More Models and Applications Pt. 2
Module 4 Introduction, Normal Approximation Pt. 1, Normal Approximation Pt. 2, Normal Approximation Pt. 3, Normal Approximation Pt. 4, Normal Approximation Pt. 5, Large-Sample Theory Pt. 1, Large-Sample Theory Pt. 2, Large-Sample Theory Pt. 3, Large-Sample Theory Pt. 4, Large-Sample Theory Pt. 5, Large-Sample Theory Pt. 6, Frequency Properties Pt. 1, Frequency Properties Pt. 2
Module 5 Introduction, Parameterized Priors and Exchangeability Pt. 1, Parameterized Priors and Exchangeability Pt. 2, Hierarchical Models Pt. 1, Hierarchical Models Pt. 2, Hierarchical Models Pt. 3, Hierarchical Models Pt. 4, Model Validation Pt. 1, Model Validation Pt. 2
Module 6 Introduction, Numerical Methods and Approximation Pt. 1, Numerical Methods and Approximation Pt. 2, Numerical Methods and Approximation Pt. 3, Simulation Techniques for Bayesian Inference Pt. 1, Simulation Techniques for Bayesian Inference Pt. 2, Simulation Techniques for Bayesian Inference Pt. 3, Simulation Techniques for Bayesian Inference Pt. 4, Markov Chain Methods Pt. 1, Markov Chain Methods Pt. 2, Markov Chain Methods Pt. 3, Markov Chain Methods Pt. 4
Module 7 Introduction, Foundations of Bayesian Regression Analysis Pt. 1, Foundations of Bayesian Regression Analysis Pt. 2, Foundations of Bayesian Regression Analysis Pt. 3, Foundations of Bayesian Regression Analysis Pt. 4, Foundations of Bayesian Regression Analysis Pt. 5, Foundations of Bayesian Regression Analysis Pt. 6, Foundations of Bayesian Regression Analysis Pt. 7, Hierarchical Linear Models Pt. 1, Hierarchical Linear Models Pt. 2, Hierarchical Linear Models Pt. 3, Hierarchical Linear Models Pt. 4, Generalized Linear Models Pt. 1, Generalized Linear Models Pt. 2, Generalized Linear Models Pt. 3, Generalized Linear Models Pt. 4, Generalized Linear Models Pt. 5, Generalized Linear Models Pt. 6, Generalized Linear Models Pt. 7
Module 8 Introduction, Setting Up and Interpreting Mixture Models Pt. 1, Setting Up and Interpreting Mixture Models Pt. 2, Setting Up and Interpreting Mixture Models Pt. 3, Setting Up and Interpreting Mixture Models Pt. 4, Setting Up and Interpreting Mixture Models Pt. 5, Applications of Mixture Models Pt. 1, Applications of Mixture Models Pt. 2, Applications of Mixture Models Pt. 3
Summative Course Assessment