Abstract
Probability elicitation is the process of formulating a person's knowledge and beliefs about one or more uncertain quantities into a joint probability distribution for those quantities. The important point is that the goal of elicitation is to capture a person's knowledge and beliefs based upon their current state of information. Consequently, the results of elicitation need be only good enough to make reasoned decisions or reasonable inferences. This chapter identifies how to elicit probabilities for large conditional probability tables in Bayesian networks. This chapter looks at Bayesian networks which are statistical models to describe and visualize in a compact graphical form the probabilistic relationships between variables of interest; the nodes of a graphical structure correspond to the variables, while directed edges between the nodes encode conditional independence relationships between them.
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