In Chapter 1, Introducing the Probability Theory, we learned about the Bayes theorem as the relation between conditional probabilities of two random variables such as A and B. This theorem is the basis for updating beliefs or model parameter values in Bayesian inference, given the observations. In this chapter, a more formal treatment of Bayesian inference will be given. To begin with, let us try to understand how uncertainties in a real-world problem are treated in Bayesian approach.
Learning Bayesian Models with R
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Learning Bayesian Models with R
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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
Free Chapter
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