Basics of Bayesian Learning - Basically Bayes
Jan Larsen
In: IEEE Workshop on Machine Learning for Signal Processing, 6-8 Sep 2006, Maynooth, Ireland.

Abstract

The tutorial focuses on the basic elements of Bayesian learning and its relation to classical learning paradigms. This includes a critical discussion of the pros and cons. The theory is illustrated by specific models and examples. Content: * Why Bayesian learning? * Basic ingredients * Bayes estimators * More on selection of priors * Generalization and bias/variance * Generalization estimation * Bayesian model selection * Discussion of Bayesian framework * Example of Bayesian learning: RVM * Bayesian signal detection

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