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Volumn 31, Issue , 2013, Pages 527-535

A recursive estimate for the predictive likelihood in a topic model

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS;

EID: 84954229808     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper
Times cited : (9)

References (15)
  • 1
    • 0037361675 scopus 로고    scopus 로고
    • Marginal likelihood and Bayes factors for Dirichlet process mixture models
    • S. Basu and S. Chib. Marginal likelihood and Bayes factors for Dirichlet process mixture models. Jour- nal of the American Statistical Association, 98(461): 224-35, 2003.
    • (2003) Jour- nal of the American Statistical Association , vol.98 , Issue.461 , pp. 224-235
    • Basu, S.1    Chib, S.2
  • 2
    • 0042107603 scopus 로고    scopus 로고
    • Objective Bayesian methods for model selection: Introduction and comparison
    • of Institute of Mathematical Statistics Lecture Notes - Monograph Series, Beachwood
    • J. O. Berger and L. Pericchi. Objective Bayesian methods for model selection: Introduction and comparison. In Model Selection, volume 38 of Institute of Mathematical Statistics Lecture Notes - Monograph Series, pages 135-207. Beachwood, 2001.
    • (2001) Model Selection , vol.38 , pp. 135-207
    • Berger, J.O.1    Pericchi, L.2
  • 3
    • 84861170800 scopus 로고    scopus 로고
    • Probabilistic topic models
    • D. Blei. Probabilistic topic models. Communications of the ACM, 55(4): 77-84, 2012.
    • (2012) Communications of the ACM , vol.55 , Issue.4 , pp. 77-84
    • Blei, D.1
  • 9
    • 0000600219 scopus 로고    scopus 로고
    • A solution to plato's problem: The latent semantic analysis theory of acquisition, induction and representation of knowledge
    • T. K. Landauer and S. T. Dumais. A solution to plato's problem: The latent semantic analysis theory of acquisition, induction and representation of knowledge. Psychological Review, 104(2): 211-240, 1997.
    • (1997) Psychological Review , vol.104 , Issue.2 , pp. 211-240
    • Landauer, T.K.1    Dumais, S.T.2
  • 10
    • 80051745679 scopus 로고    scopus 로고
    • Particle learning for sequential Bayesian computation (with discussion
    • Oxford University Press
    • H. Lopes, C. M. Carvalho, M. Johannes, and N. G. Polson. Particle learning for sequential Bayesian computation (with discussion). In Bayesian Statis- tics 9. Oxford University Press, 2011.
    • (2011) Bayesian Statis- tics , vol.9
    • Lopes, H.1    Carvalho, C.M.2    Johannes, M.3    Polson, N.G.4
  • 12
    • 85041412529 scopus 로고    scopus 로고
    • Semisupervised classification of texts using particle learning for probabilistic automata
    • Oxford University Press
    • A. Sales, C. Challis, P. R., and D. Merl. Semisupervised classification of texts using particle learning for probabilistic automata. In Bayesian Theory and Applications. Oxford University Press, 2012.
    • (2012) Bayesian Theory and Applications
    • Sales, A.1    Challis, C.2    Merl, D.3
  • 13
    • 35148901361 scopus 로고    scopus 로고
    • Nested sampling for general Bayesian computation
    • J. Skilling. Nested sampling for general Bayesian computation. Bayesian Analysis, 1(4): 833-60, 2006.
    • (2006) Bayesian Analysis , vol.1 , Issue.4 , pp. 833-860
    • Skilling, J.1
  • 14
    • 84890042115 scopus 로고    scopus 로고
    • On estimation and selection for topic models
    • M. Taddy. On estimation and selection for topic models. In AISTATS, 2012.
    • (2012) AISTATS
    • Taddy, M.1


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