Synopsis

At its core, Bayesian ML is about making predictions from noisy and imperfect data. These predictions rely on the posterior distribution, which combines a priori assumptions about the unknown quantities with a likelihood model for the observed data. This tutorial introduces classical themes in Bayesian analysis. We will start with fundamentals of random variables and conditional distributions, building into the well-known “Bayes Rule”. From here, we will dive into hypothesis testing and parameter estimation, including how to perform inference in these setups. Finally, we will showcase a flexible and interpretable Bayesian model for functional connectomics.