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Introduction to Bayesian Methods and Decision Theory

  • Simon P. Wilson
  • Rozenn Dahyot
  • Pádraig Cunningham
Part of the Cognitive Technologies book series (COGTECH)

Abstract

Bayesian methods are a class of statistical methods that have some appealing properties for solving problems in machine learning, particularly when the process being modelled has uncertain or random aspects. In this chapter we look at the mathematical and philosophical basis for Bayesian methods and how they relate to machine learning problems in multimedia. We also discuss the notion of decision theory, for making decisions under uncertainty, that is closely related to Bayesian methods. The numerical methods needed to implement Bayesian solutions are also discussed. Two specific applications of the Bayesian approach that are often used in machine learning – naïve Bayes and Bayesian networks – are then described in more detail.

Keywords

Posterior Distribution Markov Chain Monte Carlo Bayesian Method Decision Theory Subjective Probability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Simon P. Wilson
    • 1
  • Rozenn Dahyot
    • 1
  • Pádraig Cunningham
    • 2
  1. 1.Trinity College DublinIreland
  2. 2.University College DublinIreland

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