Graphical Models

Graphical Models

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时间:2019-08-05

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1、GraphicalModelsJinlongWu&TiejunLiNov20081IntroductionJordan[3]presentesaveryconciseintroductiontoGraphicalModels(GMs):Graphicalmodelsareamarriagebetweenprobabilitytheoryandgraphtheory.Theyprovideanaturaltoolfordealingwithtwoproblemsthatoccurthroughoutapplied

2、mathematicsandengineering—uncer-taintyandcomplexity—andinparticulartheyareplayinganincreasinglyimportantroleinthedesignandanalysisofmachinelearningalgorithms.Fundamentaltotheideaofagraphicalmodelisthenotionofmodularity—acomplexsystemisbuiltbycombiningsimpler

3、parts.Probabilitytheoryprovidesthegluewherebythepartsarecombined,ensuringthatthesystemasawholeisconsistent,andprovidingwaystointerfacemodelstodata.Thegraphtheoreticsideofgraphicalmodelsprovidesbothanintuitivelyappeal-inginterfacebywhichhumanscanmodelhighly-i

4、nteractingsetsofvariablesaswellasadatastructurethatlendsitselfnaturallytothedesignofefficientgeneral-purposealgorithms.Manyoftheclassicalmultivariateprobabilisticsystemsstudiedinfieldssuchasstatistics,systemsengineering,informationtheory,patternrecognitionands

5、tatisticalmechanicsarespecialcasesofthegeneralgraphicalmodelformalism—examplesincludemixturemodels,factoranalysis,hiddenMarkovmodels,KalmanfiltersandIsingmodels.Thegraphicalmodelframeworkprovidesawaytoviewallofthesesystemsasinstancesofacommonunderlyingformali

6、sm.Thisviewhasmanyadvantages—inparticular,specializedtechniquesthathavebeendevelopedinonefieldcanbetransferredbetweenresearchcommunitiesandexploitedmorewidely.Moreover,thegraphicalmodelformalismprovidesanaturalframeworkforthedesignofnewsystems.GMsareusuallydi

7、videdintotwotypes—undirectedanddirected.UndirectedGMsarealsocalledMarkovNetworksorMarkovRandomFields(MRFs),anddirectedGMsarealsoknownasBayesianNetworks(BNs),beliefnetworks,generativemodelsorcausalmodels.1.1DirectedGMs(BayesianNetworks)[4]InBayesianNetworks(B

8、Ns)eachvertexrepresentsarandomvariable,andanarcfromvertexXtovertexY(wealsosaidthatXisoneoftheparentsofY)meansXisoneofthereasonswhyYhappens,i.e.,XcausesY.HenceBNsareacyclic.BNsassumethatavariable

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