Day 1: Overview, review of basic probability theory
Notebook for exercise 1.11: normal_2d.ipynb
Day 2: Sampling algorithm, Monte-Carlo integration
Notebook for exercise 2.3: MC_integration.ipynb
Notebook for exercise 2.8: rejection_sampling.ipynb
Day 3: Markov chain!
Day 4: Monte-Carlo Markov chain method (Metropolis-Hasting algorithm)
Notebook for exercise 4.3: MH_discrete.ipynb
Notebook for exercise 4.4: MH_continuous.ipynb
Day 5: Bayesian inference
Main notebook: chp_02_modified,ipynb
Project 1: Bayes Factors and Marginal Likelihood
Project 2: Updating priors
Project 3: Approximate Bayesian computations
Project 4: Model comparison
https://discourse.pymc.io/t/brokenprocesspool-with-sample-smc-example/16701