Familiarize yourself with probabilistic graphical models through real-world 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 real-world 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
Probabilistic graphical models (PGM, also known as graphical models) are a marriage between probability theory and graph theory. Generally, PGMs use a graph-based 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 step-by-step explanation of each mathematical concept, which will help you build and analyze your own machine learning models and apply them to real-world problems. The mathematics is kept simple and each formula is explained thoroughly.
Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the code file.