Bayesian Methods for Management and Business: Pragmatic Solutions for Real Problems

387 Pages·2014·4.105 MB·English

by  Eugene D. Hahn

BAYESIAN METHODS
FOR MANAGEMENT
AND BUSINESS

BAYESIAN METHODS
FOR MANAGEMENT
AND BUSINESS
Pragmatic Solutions for Real
Problems
EUGENED.HAHN
DepartmentofInformationandDecisionSystems
SalisburyUniversity
Salisbury,MD
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LibraryofCongressCataloging-in-PublicationData:
Hahn,EugeneD.
Bayesianmethodsformanagementandbusiness:pragmaticsolutionsforreal
problems/EugeneD.Hahn,DepartmentofInformationandDecisionSystems,Salisbury
UniversitySalisbury,MD.
pagescm
Includesbibliographicalreferencesandindex.
ISBN978-1-118-63755-5(hardback)
1.Management–Statisticalmethods.2.Commercialstatistics.3.Bayesianstatistical
decisiontheory.
I.Title.
HD30.215.H342014
650.01′519542–dc23
2014011434
PrintedintheUnitedStatesofAmerica
10987654321
ToGene,Nora,David,Tara,andNok.
Thankyouforallthatyoudoandhavedone.Also,thankstoJeffKottemannfor
commentsearlyinthebook’sdevelopment.

CONTENTS
Preface xv
1 IntroductiontoBayesianMethods 1
1.1 BayesianMethods:AnAerialSurvey, 1
1.1.1 InformalExample, 3
1.2 Bayes’Theorem, 4
1.3 Bayes’TheoremandtheFocusGroup, 6
1.4 TheFlavorsofProbability, 8
1.4.1 CommonGround, 9
1.4.2 Frequency-BasedProbability, 9
1.4.3 SubjectiveProbability, 10
1.5 Summary, 11
1.6 NotationIntroducedinthisChapter, 11
2 AFirstLookatBayesianComputation 12
2.1 GettingStarted, 12
2.2 SelectingtheLikelihoodFunction, 13
2.3 SelectingtheFunctionalForm, 16
2.4 SelectingthePrior, 17
2.5 FindingtheNormalizingConstant, 18
2.6 ObtainingthePosterior, 19
2.7 CommunicatingFindings, 23
2.8 PredictingFutureOutcomes, 26
2.9 Summary, 28
2.10 Exercises, 28
vii
viii CONTENTS
2.11 NotationIntroducedinthisChapter, 29
3 Computer-AssistedBayesianComputation 30
3.1 GettingStarted, 30
3.2 RandomNumberSequences, 31
3.3 MonteCarloIntegration, 33
3.4 MonteCarloSimulationforInference, 36
3.4.1 TestingforaDifferenceinProportions, 37
3.4.2 PredictingCustomerBehavior, 38
3.4.3 PredictingCustomerBehavior,Part2, 40
3.5 TheConjugateNormalModel, 40
3.5.1 TheConjugateNormalModel:MeanwithVariance
Known, 40
3.5.2 TheConjugateNormalModel:VariancewithMean
Known, 42
3.5.3 TheConjugateNormalModelwithMean
andVarianceBothUnknown, 44
3.6 InPractice:InferencefortheConjugateNormalModel, 45
3.6.1 ConjugateNormalMeanwithVarianceKnown, 46
3.6.2 ConjugateNormalVariancewithMeanKnown, 47
3.6.3 ConjugateNormalMeanandVarianceBoth
Unknown, 48
3.7 CountDataandtheConjugatePoissonModel, 52
3.7.1 InDetail:ConjugatePoissonModelDevelopment, 53
3.7.2 InPractice:InferencefortheConjugatePoisson
Model, 54
3.8 Summary, 56
3.9 Exercises, 56
3.10 NotationIntroducedinthisChapter, 58
3.11 Appendix—InDetail:FindingPosteriorDistributions
fortheNormalModel, 58
3.11.1 AnalysisoftheNormalMeanwithVariance
Known, 59
3.11.2 AnalysisoftheNormalVariancewithMean
Known, 61
3.11.3 AnalysisoftheConjugateNormalModelwithMean
andVarianceBothUnknown, 62
4 MarkovChainMonteCarloandRegressionModels 64
4.1 IntroductiontoMarkovChainMonteCarlo, 64
4.2 FundamentalsofMCMC, 66
4.3 GibbsSampling, 67