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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

Sum-product and belief updates

When computing the distribution of one variable (or a subset of variables), the main operation is the marginalization, which consists of summing over a variable (or a subset of variables) to eliminate it from the main expression. If we call ϕ a factor in the factorization of the joint distribution, we can use the following properties to generalize and improve the variable elimination algorithm we saw in the previous section:

  • Commutativity: Sum-product and belief updates
  • Associativity: Sum-product and belief updates
  • And if Sum-product and belief updates

If again we apply it to the joint distribution P(ABCD) from the previous section, we again obtain:

Sum-product and belief updates

In the end, the main expression that comes again and again is the sum-product over a factor, which can be written as Sum-product and belief updates.

So in general, if we can find a good ordering of the factors or the variables in the case of a directed graphical model as we saw until now, we can, by applying the previous sum-product formula, eliminate step by step each variable until we obtain the desired subset.

The ordering must...

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