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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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
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