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

13.12 Nonparametric Bayesian Modeling

The Bayesian approach to parametric modeling has been the focus of our attention in the current and previous chapters. The underlying assumption was that the number of the unknown parameters was fixed and finite. We now turn our attention to a more general task. We will assume that the hidden structure of our model is not fixed but is allowed to grow with the data. In other words, its complexity is not specified a priori but is left to be determined from the data. This is the reason that such models are called nonparametric; recall from Chapter 3 that a model is called parametric if the number of free parameters is fixed and independent of the size of the data set.

We will avoid treating nonparametric Bayesian ...

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