2004 | OriginalPaper | Chapter
Bayesian Inference: An Introduction to Principles and Practice in Machine Learning
Author : Michael E. Tipping
Published in: Advanced Lectures on Machine Learning
Publisher: Springer Berlin Heidelberg
Included in: Professional Book Archive
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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’.