Book description
Familiarize yourself with probabilistic graphical models through realworld problems and illustrative code examples in R
About This Book
 Predict and use a probabilistic graphical models (PGM) as an expert system
 Comprehend how your computer can learn Bayesian modeling to solve realworld problems
 Know how to prepare data and feed the models by using the appropriate algorithms from the appropriate R package
Who This Book Is For
This book is for anyone who has to deal with lots of data and draw conclusions from it, especially when the data is noisy or uncertain. Data scientists, machine learning enthusiasts, engineers, and those who curious about the latest advances in machine learning will find PGM interesting.
What You Will Learn
 Understand the concepts of PGM and which type of PGM to use for which problem
 Tune the model's parameters and explore new models automatically
 Understand the basic principles of Bayesian models, from simple to advanced
 Transform the old linear regression model into a powerful probabilistic model
 Use standard industry models but with the power of PGM
 Understand the advanced models used throughout today's industry
 See how to compute posterior distribution with exact and approximate inference algorithms
In Detail
Probabilistic graphical models (PGM, also known as graphical models) are a marriage between probability theory and graph theory. Generally, PGMs use a graphbased representation. Two branches of graphical representations of distributions are commonly used, namely Bayesian networks and Markov networks. R has many packages to implement graphical models.
We'll start by showing you how to transform a classical statistical model into a modern PGM and then look at how to do exact inference in graphical models. Proceeding, we'll introduce you to many modern R packages that will help you to perform inference on the models. We will then run a Bayesian linear regression and you'll see the advantage of going probabilistic when you want to do prediction.
Next, you'll master using R packages and implementing its techniques. Finally, you'll be presented with machine learning applications that have a direct impact in many fields. Here, we'll cover clustering and the discovery of hidden information in big data, as well as two important methods, PCA and ICA, to reduce the size of big problems.
Style and approach
This book gives you a detailed and stepbystep explanation of each mathematical concept, which will help you build and analyze your own machine learning models and apply them to realworld problems. The mathematics is kept simple and each formula is explained thoroughly.
Publisher resources
Table of contents

Learning Probabilistic Graphical Models in R
 Table of Contents
 Learning Probabilistic Graphical Models in R
 Credits
 About the Author
 About the Reviewers
 www.PacktPub.com
 Preface
 1. Probabilistic Reasoning
 2. Exact Inference
 3. Learning Parameters
 4. Bayesian Modeling – Basic Models
 5. Approximate Inference
 6. Bayesian Modeling – Linear Models
 7. Probabilistic Mixture Models
 A. Appendix
 Index
Product information
 Title: Learning Probabilistic Graphical Models in R
 Author(s):
 Release date: April 2016
 Publisher(s): Packt Publishing
 ISBN: 9781784392055
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