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

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


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’.


Bayesian Inference Bayesian Framework Test Error Predictive Distribution Marginal Likelihood 
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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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