In the previous chapter, we saw algorithms for exact inference on graphical models. The computational complexity of calculating exact inference is exponential to the tree width of the network. Hence, for much larger networks whose tree width is large, exact inference becomes infeasible. Also, in many of our real-life problems, we are not particularly concerned about the exact probabilities of random variables. Rather, we are much more interested in the relative probabilities of the states of variables. Therefore, in this chapter, we will discuss algorithms to perform approximate inference over networks. There are many algorithms for approximate inference, but the approach to find an approximate distribution remains the same in all of them. In most of these, we usually define a target class Q of easy distributions, and then from this class, we try to find the distribution that is closest to our actual distribution 
and answer inference queries from this estimated...
Mastering Probabilistic Graphical Models with Python
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Mastering Probabilistic Graphical Models with Python
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Table of Contents (14 chapters)
Mastering Probabilistic Graphical Models Using Python
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Bayesian Network Fundamentals
Markov Network Fundamentals
Inference – Asking Questions to Models
Approximate Inference
Model Learning – Parameter Estimation in Bayesian Networks
Model Learning – Parameter Estimation in Markov Networks
Specialized Models
Index
Customer Reviews