Learning Probabilistic Graphical Models in R
Familiarize yourself with probabilistic graphical models through realworld problems and illustrative code examples in R
Price  $27.99  $43.86

Rating  
Author  David Bellot 
Publisher  Packt Publishing 
Published  2016 
Pages  250 
Language  English 
Format  Paper book / ebook (PDF) 
ISBN10  1784392057 
ISBN13  9781784392055 
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.
 David Bellot
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