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