This course includes three parts: (1) Main principles of Bayesian statistics (Bayes rule, Prior, Likelihood, MCMC...), (2) Bayesian regression (model formulation, inference with MCMC), (3) Model evaluation.

IoZ statistics course, basics of Bayesian statistics - version 1

A Bayesian Statistics workshop presented to the University of Toronto 2021 ISSC (Independent Summer Statistics Community). It covers the basics of generative modelling, prior setting, model evaluation, and Stan programming. Check the completed folder for the finished files.

Random notebooks exploring basics of various Machine Learning Topics (RNNs, GANs, Monte Carlo, Bayesian Statistics)