In the previous chapter, we covered the theory of Bayesian linear regression in some detail. In this chapter, we will take a sample problem and illustrate how it can be applied to practical situations. For this purpose, we will use the generalized linear model (GLM) packages in R. Firstly, we will give a brief introduction to the concept of GLM to the readers.

Learning Bayesian Models with R
By :

Learning Bayesian Models with R
By:
Overview of this book
Table of Contents (16 chapters)
Learning Bayesian Models with R
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Introducing the Probability Theory
The R Environment
Introducing Bayesian Inference
Machine Learning Using Bayesian Inference
Bayesian Regression Models
Bayesian Classification Models
Bayesian Models for Unsupervised Learning
Bayesian Neural Networks
Bayesian Modeling at Big Data Scale
Index
Customer Reviews