Bayesian Modelling for Machine Learning

Bayesian Modelling for Machine Learning

Paul Rippon (The University of Newcastle, Australia) and Kerrie Mengersen (The University of Newcastle, Australia)
DOI: 10.4018/978-1-59904-941-0.ch024
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Abstract

Learning algorithms are central to pattern recognition, artificial intelligence, machine learning, data mining, and statistical learning. The term often implies analysis of large and complex data sets with minimal human intervention. Bayesian learning has been variously described as a method of updating opinion based on new experience, updating parameters of a process model based on data, modelling and analysis of complex phenomena using multiple sources of information, posterior probabilistic expectation, and so on. In all of these guises, it has exploded in popularity over recent years.

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