In this chapter, we covered basic principles of Bayesian inference. Starting with how uncertainty is treated differently in Bayesian statistics compared to classical statistics, we discussed deeply various components of Bayes' rule. Firstly, we learned the different types of prior distributions and how to choose the right one for your problem. Then we learned the estimation of posterior distribution using techniques such as MAP estimation, Laplace approximation, and MCMC simulations. Once the readers have comprehended this chapter, they will be in a position to apply Bayesian principles in their data analytics problems. Before we start discussing specific Bayesian machine learning problems, in the next chapter, we will review machine learning in general.

#### 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