In this talk, we lay the ground for Markov Chain Monte Carlo (MCMC) methods to inferring the posterior distribution, after looking into the limitations posed by the analytical approach. We demonstrate how MCMC works and use it to solve a Bayesian regression problem and introduce concepts related to causal inference. Before doing so, we have a quick tutorial on using Pyro as a probabilistic programming language.
Statistical Rethinking: A Bayesian Course with Examples in R and Stan,
Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today's model-based statistics, the book pushes you to perform step-by-step calculations that are usually automated. This unique computational approach ensures that you understand enough of the details to make reasonable …