The posterior probability is given by the left-hand term of the equation [P(H|E, c)]. It represents the probability of hypothesis H after considering the effect of evidence E on past experience c. The term P(H|c) is the a-priori probability of H given c alone. Thus, the a priori probability can be viewed as the subjective belief of occurrence of hypothesis H based upon past experience. The likelihood, represented by the term P(E|H,c), gives the probability of the evidence assuming the hypothesis H and the background information c is true. The term P(E|c) is independent of H and is regarded as a normalizing or scaling factor (Niedermayer, 2003). Thus, Bayesian networks provide a methodology for combining subjective beliefs with available evidence
The posterior probability is given by the left-hand term of the equation [P(H|E, c)]. It represents the probability of hypothesis H after considering the effect of evidence E on past experience c. The term P(H|c) is the a-priori probability of H given c alone. Thus, the a priori probability can be viewed as the subjective belief of occurrence of hypothesis H based upon past experience. The likelihood, represented by the term P(E|H,c), gives the probability of the evidence assuming the hypothesis H and the background information c is true. The term P(E|c) is independent of H and is regarded as a normalizing or scaling factor (Niedermayer, 2003). Thus, Bayesian networks provide a methodology for combining subjective beliefs with available evidence
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