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Bayesian Inference: An Introduction to Principles and Practice in Machine Learning

  • Michael E. Tipping
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3176)

Abstract

This article gives a basic introduction to the principles of Bayesian inference in a machine learning context, with an emphasis on the importance of marginalisation for dealing with uncertainty. We begin by illustrating concepts via a simple regression task before relating ideas to practical, contemporary, techniques with a description of ‘sparse Bayesian’ models and the ‘relevance vector machine’.

Keywords

Bayesian Inference Bayesian Framework Test Error Predictive Distribution Marginal Likelihood 
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 2004

Authors and Affiliations

  • Michael E. Tipping
    • 1
  1. 1.Microsoft ResearchCambridgeU.K.

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