Book Image

Learning Probabilistic Graphical Models in R

By : David Bellot, Dan Toomey
Book Image

Learning Probabilistic Graphical Models in R

By: David Bellot, Dan Toomey

Overview of this book

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.
Table of Contents (15 chapters)

About the Reviewers

Mzabalazo Z. Ngwenya holds a postgraduate degree in mathematical statistics from the University of Cape Town. He has worked extensively in the field of statistical consulting and has considerable experience working with R. Areas of interest to him are primarily centered around statistical computing. Previously, he has been involved in reviewing the following Packt Publishing titles: Learning RStudio for R Statistical Computing, Mark P.J. van der Loo and Edwin de Jonge; R Statistical Application Development by Example Beginner's Guide, Prabhanjan Narayanachar Tattar; Machine Learning with R, Brett Lantz; R Graph Essentials, David Alexandra Lillis; R Object-oriented Programming, Kelly Black; Mastering Scientific Computing with R, Paul Gerrard and Radia Johnson; and Mastering Data Analysis with R, Gergely Darócz.

Prabhanjan Tattar is currently working as a senior data scientist at Fractal Analytics, Inc. He has 8 years of experience as a statistical analyst. Survival analysis and statistical inference are his main areas of research/interest. He has published several research papers in peer-reviewed journals and authored two books on R: R Statistical Application Development by Example, Packt Publishing; and A Course in Statistics with R, Wiley. The R packages gpk, RSADBE, and ACSWR are also maintained by him.