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Learning Probabilistic Graphical Models in R
Learning Probabilistic Graphical Models in R
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 (10 chapters)
Preface
Free Chapter
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
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