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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

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Appendices

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By G. Alexi Rodríguez-Arelis, Hedayat Zarkoob, Michael Gelbart, and Trevor Campbell