EVSI - Decision Making Under Risk Continued: BayesTheorem...

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Decision Making Under Risk Continued: Bayes’Theorem and Posterior Probabilities MGS3100 - Chapter 8 Slides 8c
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Bayesian Methods There is a continuing debate among statisticians, little known to those outside the field, over the proper definition of probability . The frequentist definition sees probability as the long-run expected frequency of occurrence. P(A) = n/N, where n is the number of times event A occurs in N opportunities. The Bayesian view of probability is related to degree of belief. It is a measure of the plausibility of an event given incomplete knowledge.
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The Market Research Question Urn 1 8 Red 2 White P(Red/Urn1) = 8/10 = 0.8 (Known Population) Have P(A/B) Urn 2 ? R What is in the urn? Select a sample, and then make inferences about the population (Unknown Population) Want P(B/A) Vs.
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How We Will Use Bayes' Theorem Prior information can be based on the results of previous experiments, or expert opinion, and can be expressed as probabilities. If it is desirable to improve on this state of knowledge, an experiment can be conducted. Bayes' Theorem is the mechanism used to update the state of knowledge with the results of the experiment to provide a posterior distribution.
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Bayes’ Theorem Used to revise probabilities based upon new data Posterior probabilities Prior probabilities New data
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How Bayes' Theorem Works Let the experiment be A and the prediction be B. Let’s assume that both have occurred. The probability of both A and B together is P(A∩B), or simply P(AB). The law
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This note was uploaded on 01/07/2011 for the course MGS 3100 taught by Professor Kin during the Three '10 term at Southern Queensland.

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EVSI - Decision Making Under Risk Continued: BayesTheorem...

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