A primer on probabilistic inference

Thomas L. Griffiths, Alan Yuille

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationThe Probabilistic Mind
Subtitle of host publicationProspects for Bayesian cognitive science
PublisherOxford University Press
ISBN (Electronic)9780191695971
ISBN (Print)9780199216093
DOIs
StatePublished - Mar 22 2012

All Science Journal Classification (ASJC) codes

  • Psychology(all)

Keywords

  • Bayesian inference
  • Cognition
  • EM algorithm
  • Markov chain monte carlo
  • Probabilistic models

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    Griffiths, T. L., & Yuille, A. (2012). A primer on probabilistic inference. In The Probabilistic Mind: Prospects for Bayesian cognitive science Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199216093.003.0002