Chapter 5. Modeling dependencies with Bayesian and Markov networks

5.1. Modeling dependencies

5.1.1. Directed dependencies

5.1.2. Undirected dependencies

5.1.3. Direct and indirect dependencies

5.2. Using Bayesian networks

5.2.1. Bayesian networks defined

5.2.2. How a Bayesian network defines a probability distribution

5.2.3. Reasoning with Bayesian networks

5.3. Exploring a Bayesian network example

5.3.1. Designing a computer system diagnosis model

5.3.2. Reasoning with the computer system diagnosis model

5.4. Using probabilistic programming to extend Bayesian networks: pred- dicting product success

5.4.1. Designing a product success prediction model

5.4.2. Reasoning with the product success prediction model

5.5. Using Markov networks

5.5.1. Markov networks defined

5.5.2. Representing and reasoning with Markov networks

5.6. Summary

5.7. Exercises

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