Dependent Normalized Random Measures

dc.contributor.authorChen, Changyou
dc.contributor.authorRao, Vinayak
dc.contributor.authorBuntine, Wray
dc.contributor.authorTeh, Yee Whye
dc.coverage.spatialAtlanta United States of America
dc.date.accessioned2015-12-10T23:19:59Z
dc.date.createdJune 16-21 2013
dc.date.issued2013
dc.date.updated2015-12-10T10:19:28Z
dc.description.abstractIn this paper we propose two constructions of dependent normalized random measures, a class of nonparametric priors over dependent probability measures. Our constructions, which we call mixed normalized random measures (MNRM) and thinned normalized random measures (TNRM), involve (respectively) weighting and thinning parts of a shared underlying Poisson process before combining them together. We show that both MNRM and TNRM are marginally normalized random measures, resulting in well understood theoretical properties. We develop marginal and slice samplers for both models, the latter necessary for inference in TNRM. In time-varying topic modeling experiments, both models exhibit superior performance over related dependent models such as the hierarchical Dirichlet process and the spatial normalized Gamma process.
dc.identifier.urihttp://hdl.handle.net/1885/66136
dc.publisherMIT Press
dc.relation.ispartofseries30th International Conference on Machine Learning ICML 2013
dc.sourceThe Sample-Complexity of General Reinforcement Learning
dc.source.urihttp://jmlr.org/proceedings/papers/v28/
dc.titleDependent Normalized Random Measures
dc.typeConference paper
local.bibliographicCitation.lastpage9
local.bibliographicCitation.startpage1
local.contributor.affiliationChen, Changyou, College of Engineering and Computer Science, ANU
local.contributor.affiliationRao, Vinayak, Duke University
local.contributor.affiliationBuntine, Wray, College of Engineering and Computer Science, ANU
local.contributor.affiliationTeh, Yee Whye, University of Oxford
local.contributor.authoremailu4814481@anu.edu.au
local.contributor.authoruidChen, Changyou, u4814481
local.contributor.authoruidBuntine, Wray, u1817485
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080199 - Artificial Intelligence and Image Processing not elsewhere classified
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
local.identifier.ariespublicationu4334215xPUB1227
local.identifier.scopusID2-s2.0-84897505920
local.identifier.uidSubmittedByu4334215
local.type.statusPublished Version

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