Bayesian inference: An introduction to principles and practice in machine learning. (English) Zbl 1120.68438
Bousquet, Olivier (ed.) et al., Advanced lectures on machine learning. ML summer schools 2003, Canberra, Australia, February 2--14, 2003, Tübingen, Germany, August 4--16, 2003. Revised lectures. Berlin: Springer (ISBN 3-540-23122-6/pbk). Lecture Notes in Computer Science 3176. Lecture Notes in Artificial Intelligence, 41-62 (2004).
Summary: 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’. For the entire collection see [Zbl 1120.68002
|68T05||Learning and adaptive systems|