資源描述:
《multidomain sampling with applications to structural inference of bayesian networks》由會(huì)員上傳分享,免費(fèi)在線閱讀,更多相關(guān)內(nèi)容在工程資料-天天文庫(kù)。
1、Thisarticlewasdownloadedby:[RenminUniversityofChina]On:05April2012,At:23:50Publisher:Taylor&FrancisInformaLtdRegisteredinEnglandandWalesRegisteredNumber:1072954Registeredoffice:MortimerHouse,37-41MortimerStreet,LondonW1T3JH,UKJournaloftheAmericanStatisticalAssociationPublicationdetails,in
2、cludinginstructionsforauthorsandsubscriptioninformation:http://www.tandfonline.com/loi/uasa20Multi-DomainSamplingWithApplicationstoStructuralInferenceofBayesianNetworksQingZhouQingZhouisAssistantProfessor,DepartmentofStatistics,UniversityofCalifornia,LosAngeles,CA90095.Thisworkwassupporte
3、dinpartbyNSFgrantDMS-0805491andNSFCAREERAwardDMS-1055286.Theauthorthankstheeditor,theassociateeditor,andthetworefereesforhelpfulcommentsandsuggestionswhichsignificantlyimprovedthemanuscript.Availableonline:24Jan2012Tocitethisarticle:QingZhou(2011):Multi-DomainSamplingWithApplicationstoStr
4、ucturalInferenceofBayesianNetworks,JournaloftheAmericanStatisticalAssociation,106:496,1317-1330Tolinktothisarticle:http://dx.doi.org/10.1198/jasa.2011.ap10346PLEASESCROLLDOWNFORARTICLEFulltermsandconditionsofuse:http://www.tandfonline.com/page/terms-and-conditionsThisarticlemaybeusedforre
5、search,teaching,andprivatestudypurposes.Anysubstantialorsystematicreproduction,redistribution,reselling,loan,sub-licensing,systematicsupply,ordistributioninanyformtoanyoneisexpresslyforbidden.Thepublisherdoesnotgiveanywarrantyexpressorimpliedormakeanyrepresentationthatthecontentswillbecom
6、pleteoraccurateoruptodate.Theaccuracyofanyinstructions,formulae,anddrugdosesshouldbeindependentlyverifiedwithprimarysources.Thepublishershallnotbeliableforanyloss,actions,claims,proceedings,demand,orcostsordamageswhatsoeverorhowsoevercausedarisingdirectlyorindirectlyinconnectionwithoraris
7、ingoutoftheuseofthismaterial.Supplementarymaterialsforthisarticleareavailableonline.PleaseclicktheJASAlinkathttp://pubs.amstat.org.Multi-DomainSamplingWithApplicationstoStructuralInferenceofBayesianNetworksQingZHOUWhenaposteriordistributionhasmultiplemodes,unconditi