In this article, which is a supplementary article to the TICS July Special Issue on probabilistic models of cognition, we will introduce some of the tools that can be used to address these challenges. The plan of the article is as follows. First, we introduce Bayesian inference, which is at the heart of many probabilistic models. We then consider how to define structured probability distributions, introducing some of the key ideas behind graphical models, which can be used to represent the dependencies among a set of variables. Finally, we discuss two algorithms that are used to evaluate the predictions of probabilistic models: the Expectation-Maximization (EM) algorithm, and Markov chain Monte Carlo (MCMC). Several books provide a more detailed discussion of these topics in the context of statistics [8–10], machine learning [11–13], and artificial intelligence [14–16].