In this chapter, we discussed special cases in graphical models that are widely used in the real world. We discussed the Naive Bayes model, which is a very simple model but is widely used in text classification and is known to give very good results. Then, we talked about DBNs, which are generally used in cases where we want to model some problem in which the values of the variables change with time. We discussed the Hidden Markov model, which is a very simple case of the DBN and is widely used in the field of speech recognition.
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