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