The LW sampling algorithm correctly samples the posterior of the descendant nodes, but for the nondescendants, it samples the prior and tries to fix it with the weightings. So, for the case where we have most of the observed nodes as leaves of the network, we would be sampling the prior rather than the posterior. We will now discuss an algorithm that generates a sequence of samples. The first samples generated may be near to the prior, but as we keep on generating samples, it keeps getting closer to the posterior. Also, this sampling algorithm works for both Bayesian and Markov networks.
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