A primer on probabilistic inference
Thomas L. Griffiths, Alan Yuille · 2008
Abstract This chapter provides the technical introduction to Bayesian methods. Probabilistic models of cognition are often referred to as Bayesian models, reflecting the central role that Bayesian inference plays in reasoning under uncertainty. It introduces the basic ideas of Bayesian inference and discusses how it can be used in different contexts. Probabilistic models provide a unique opportunity to develop a rational account of human cognition that combines statistical learning with structured representations. It recommends the EM algorithm and Markov chain Monte Carlo to estimate the parameters of models that incorporate latent variables, and to work with complicated probability distributions of the kind that often arise in Bayesian inference.