Skip to Main Content
Machine Learning
book

Machine Learning

by Sergios Theodoridis
April 2015
Intermediate to advanced content levelIntermediate to advanced
1062 pages
40h 35m
English
Academic Press
Content preview from Machine Learning
Chapter 13

Bayesian Learning

Approximate Inference and Nonparametric Models

Abstract

This chapter is the second one dedicated to Bayesian learning. The emphasis here is on more advanced topics, dealing with approximate inference methods. Two paths for approximate inference, known as variational techniques, are discussed. One is based on the mean field approximation and the lower bound interpretation of the EM, and the other on convex duality and variational bounds. Regression and mixture modeling are discussed in this framework. Emphasis is given to sparse Bayesian modeling techniques and hierarchical Bayesian models. The relevance vector machine framework is presented. Expectation propagation is also discussed as an alternative to variational ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Machine Learning

Machine Learning

Mohssen Mohammed, Muhammad Badruddin Khan, Eihab Mohammed Bashier
Machine Learning

Machine Learning

Subramanian Chandramouli, Saikat Dutt, Amit Kumar Das
Machine Learning Algorithms

Machine Learning Algorithms

Giuseppe Bonaccorso
Introducing Machine Learning

Introducing Machine Learning

Dino Esposito, Francesco Esposito

Publisher Resources

ISBN: 9780128015223