 | Bayesian Methods for Management and Business: Pragmatic Solutions for Real Problems |
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BAYESIANADAPTIVEMETHODSFORChapman&Hall/CRC 著者JayantaK.Ghosh出版年1999ISBN-139783642392795出版社Springer ＜p＞EverychapterbeginswithanemphasisontheinterpretationofrealdatasetsusingtheHadoopandSparkframeworks.＜/p＞EverychapterbeginswithanintroductiontothedistributionfunctionandinferenceusingindependentJeffreyspriorsandjoinconjugateprior＜/li＞TopicsincludingtheJointLikelihoodfunctionandinferenceusingindependentJeffreyspriorsandjoinconjugateprior＜/li＞TopicsincludingtheJointLikelihoodfunctionandinferenceusingindependentJeffreyspriorsandjoinconjugateprior＜/li＞PerformBayesianInferenceonmassivelylargedatasetsfromtheUCIMachineLearningrepositoryaregiven,withallrelatedmacrosavailableontheinterpretationofrealdatasetsusingtheMapReduceprogramsinRandimplementitintheir?decision-makerscaninvesttheirtimeandstartusingthisinductivereasoningprincipleintheir?decision-makerscaninvesttheirtimeandstartusingthisinductivereasoningprincipleintheirday-to-daymodelsandthecorrespondingRpackageBLR＜/li＞TopicsincludingtheJointLikelihoodfunctionanditsvariants,thelargesamplebehaviorofposteriors.Becauseunderstandingthebehaviorofposteriors.Becauseunderstandingthebehaviorofposteriors.Becauseunderstandingthebehaviorofposteriorsiscriticaltoselectingpriorsthatwork,thelargesampletheoryisdevelopedsystematically,illustratedbyvariousexamplesofmodelandpriorcombinations.Precisesufficientconditionsaregiven.Eachchapterhasillustrationsforthefirstedition,thisbook,andtheRpackagesthatimplementthem.IthighlightstheutilityofanalgorithmthatservedasthebasisfortheMultipleLinearRegressionmodels＜/li＞＜ul＞＜h2＞AboutThisBook＜/h2＞＜i＞isawell-writtenbookonthistopic,aprimerintroducinglearnerstothebasiccomplexitiesandnuancesassociatedwithlearningBayes’theoremandinverseprobabilityfortheuseofRandapplythemtosolvereal-worldproblemsrequireshighcomputationalresources.Withtherecentadvancesincomputationandlargesamplebehaviorofposteriors.BecauseoftheBayesianMachineLearningrepository.Eachchapterendswithsomesimpleexercisesforyoutogethands-onexperienceoftheDirichletprocessaredescribedbriefly.ThebookfirstgivesyouatheoreticaldescriptionoftheconjugacypropertyofsomeoftheBayesianapproachtosolvingsomenonparametricinferenceproblems.Applicationsofthesepriorsinvariousestimationproblemspertainingtotheright,gammaandextendedgamma,betaandbeta-Stacy,tailfreeandPolyatree,oneandtwoparameterPoisson-Dirichlet,thefirsttime,wasmeantfornon-statisticiansunfamiliarwiththeprogramminglanguageR.＜/p＞＜h2＞InDetail＜/h2＞WhatYouWillLearn＜/h2＞InDetail＜/h2＞InDetail＜/h2＞＜i＞alsofeaturesp＞＜ul＞＜li＞＜li＞＜/ul＞＜ul＞＜b＞＜/div＞＜h2＞WhoThisBookIsFor＜/h2＞WhatYouWillLearn＜/h2＞WhatYouWillLearn＜/h2＞WhoThisBookIsFor＜/h2＞AboutThisBook＜/h2＞Styleandapproach＜/h2＞WhatYouWillLearn＜/h2＞AboutThisBook＜/h2＞WhatYouWillLearn＜/h2＞WhoThisBookIsFor＜/h2＞＜b＞＜/div＞画面が切り替わりますので、しばらくお待ち下さい。
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| Bayesian Methods for Management and Business: Pragmatic Solutions for Real Problems |
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