Chapter

A primer on probabilistic inference

Thomas L. Griffiths and Alan Yuille

in The Probabilistic Mind:

Published in print March 2008 | ISBN: 9780199216093
Published online March 2012 | e-ISBN: 9780191695971 | DOI: http://dx.doi.org/10.1093/acprof:oso/9780199216093.003.0002
A primer on probabilistic inference

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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.

Keywords: Bayesian inference; probabilistic models; cognition; EM algorithm; Markov chain Monte Carlo

Chapter.  11625 words.  Illustrated.

Subjects: Cognitive Psychology

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