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Learning Probabilistic Graphical Models in R

You're reading from   Learning Probabilistic Graphical Models in R Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R

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Product type Paperback
Published in Apr 2016
Publisher Packt
ISBN-13 9781784392055
Length 250 pages
Edition 1st Edition
Languages
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Toc

Summary


In the second chapter, we introduced the fundamentals of inference and we saw the most important algorithms for computing posterior distribution: variable elimination and the junction tree algorithm. We learned how to build a graphical model by considering causality, temporal relationships, and by identifying patterns between variables. We saw a fundamental feature of probabilistic graphical models, which is the combination of graphs to build more complex models. And we learned how to perform inference with a junction tree algorithm in R and saw that the same junction tree can be used for any type of query, on both marginal and joint distribution. In the last section we saw several real-life examples of PGM that can be used in many applications and are usually good candidates for exact inference.

In this chapter, we faced a problem when defining a new graphical model: the parameters are tedious to determine. In fact, even on small examples it's complicated. In the next chapter we...

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