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Welcome to DSCI 553: Statistical Inference and Computation II
Lectures
Lecture Learning Objectives
Lecture 1 - Frequentist and Bayesian Overview, Probabilistic Generative Models, and
Stan
Lecture 2 - Conditional Probabilities, Bayes’ Rule, and Maximum a Posteriori Estimation
Lecture 3 - Bayesian Statistics in Action: The Beta-Binomial Model
Lecture 4 - Markov Chain Monte Carlo,
Stan
, and Complex Bayesian Models
Lecture 5 - Bayesian Normal Linear Regression and Hypothesis Testing
Lecture 6 - Bayesian Binary Logistic Regression
Lecture 7 - Bayesian Hierarchical Models
Lecture 8 - More Hierarchical Modelling and MCMC Diagnostics
Tutorial
Insights of Markov Chain Monte Carlo via the Gamma-Poisson Model
Appendices
Distribution Cheatsheet
Greek Alphabet
The Bayesian Workflow
Repository
Open issue
Index
