Chapter 14 Part 4 - Chapter 14 Decision Analysis Part 4...

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Chapter 14 Decision Analysis – Part 4
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Agenda Bayes Theorem Utility Theory HW#25 (if we have time)
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Review We’ve been able to: Create payoff table Calculate EMV and EVPI given likelihoods Can perform sensitivity analysis to determine ranges for decisionmaking Can seek additional information to get a better assessment of likelihoods Can compare EVSI to EVPI to determine the efficiency of obtaining additional information
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Sample Information Once we obtain sample information, we can identify conditional probabilities for each outcome of the survey Example: Positive lobbying, Negative lobbying With knowledge of conditional probabilities, we can use Bayes’ Theorem to compute branch probabilities (posterior probabilities)
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Bayes’ Theorem - Terms Prior Probabilities: Original likelihoods without sample information Conditional Probabilities: Probability of a sample outcome given a state of nature P (Positive Lobbying | Favorable Vote) P (Positive Lobbying | Negative Vote) P (Negative Lobbying | Favorable Vote) P (Negative Lobbying | Negative Vote)
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Bayes’ Theorem - Terms Joint Probabilities: Each Prior Probability multiplied by the Corresponding Conditional Probability Sum of Joint Probabilities: The probability of the sample condition Posterior Probabilities: Each Joint Probability divided by the sum of the Joint Probability
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Restaurant Example Prior Probabilities: Favorable Vote: .55 Unfavorable Vote: .45 Conditional Probabilities: Lobbying Results State of Nature
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This note was uploaded on 12/13/2011 for the course BUAD 346 taught by Professor Staff during the Fall '08 term at University of Delaware.

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Chapter 14 Part 4 - Chapter 14 Decision Analysis Part 4...

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