Question: In this chapter we present Bayesian networks, originally called belief networks, for the representation of uncertainty but other types of graphical models may also be

In this chapter we present Bayesian networks, originally called belief networks, for the representation of uncertainty but other types of graphical models may also be used. Consider, Markov networks, also called Markov random fields, cited in the bibliographical and historical notes. What differentiates Markov and Bayesian networks? What are advantages and disadvantages of each? What is a domain in which each network might be more suitable than the other?

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