In this chapter, we discussed different ways of performing approximate inference in graphical models, such as cluster graph belief propagation, propagation using approximate messages, and inference based on the concepts of sampling from the model. In cluster graph belief propagation, we relaxed the constraint of having a clique tree, and instead, performed belief propagation on the cluster graph. In the propagation using approximate messages, instead of relaxing the constraints on the structure of the graph, we tried to approximate the messages passed between the clusters. Next, we discussed sampling methods in detail. There are two different ways of sampling. The first includes full particles, where each sample has instantiations of all the variables of the network. The other method consists of collapsed particles, where each sample is an instantiation of a subset of the network's variables. We also discussed the problems we face in the case of full particles. In full particles...
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