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Rating 4.5 stars (1,321) Then the Bayes net could provide a classi cation decision using the highest posterior probability among p(winter | coat), p(spring | coat), p(summer | coat), and p(fall | coat) (see the exercises). A Bayesian network represents the joint probability distribution for a given set of variables. What is a joint probability distribution Let X 1 , X 2 , . . . , X m represent a set of m random variables, with each random variable X i de ned on space S X i . For example, a normal random variable X is de ned on space S X , where S X is the real number line. Then the joint space of X 1 , X 2 , . . . , X m is de ned as the cross-product S X 1 S X 2 S X m . That is, each joint observation consists of the vector of length m of observed eld values x1 , x2 , . . . , xm . The distribution of these observations over the joint space is called the joint probability distribution. The Bayesian network represents the joint probability distribution by providing (1) a speci ed set of assumptions regarding the conditional independence of the variables, and (2) the probability tables for each variable given its direct predecessors. For each variable, information regarding both is provided. For a subset of variables X 1 , X 2 , . . . , X m , the joint probability may be found thus: annotate pdf online google docs Google PDF Editor: Edit PDF in Google Docs 2019 - Updated ...
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