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Volumn 9780521196765, Issue , 2011, Pages 104-124

Two problems with variational expectation maximisation for time series models

Author keywords

[No Author keywords available]

Indexed keywords

EDUCATION; EQUIVALENCE CLASSES; MARKOV PROCESSES; MAXIMUM LIKELIHOOD; MONTE CARLO METHODS; OPTIMIZATION; STATE SPACE METHODS; TIME SERIES;

EID: 84923421297     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1017/CBO9780511984679.006     Document Type: Chapter
Times cited : (174)

References (14)
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    • Dempster, A.P.1
  • 5
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    • Another interpretation of the EM algorithm for mixture distributions
    • R. Hathaway. Another interpretation of the EM algorithm for mixture distributions. Statistics and Probability Letters, 4:53-56, 1986.
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    • Hathaway, R.1
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    • Bayesian parameter estimation via variational methods
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    • T. Jaakkola and M. Jordan. Bayesian parameter estimation via variational methods. Statistics and Computing, 10(1):25-37, January 2000.
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    • Jaakkola, T.1    Jordan, M.2
  • 7
    • 0001837853 scopus 로고    scopus 로고
    • Improving the mean field approximation via the use of mixture distributions
    • MIT Press
    • T. S. Jaakkola and M. I. Jordan. Improving the mean field approximation via the use of mixture distributions. In Learning in Graphical Models, pages 163-173. MIT Press, 1999.
    • (1999) Learning in Graphical Models , pp. 163-173
    • Jaakkola, T.S.1    Jordan, M.I.2
  • 8
    • 0033225865 scopus 로고    scopus 로고
    • An introduction to variational methods for graphical models
    • M. I. Jordan, Z. Ghahramani, T. S. Jaakkola and L. K. Saul. An introduction to variational methods for graphical models. Machine Learning, 37(2):183-233, 1999.
    • (1999) Machine Learning , vol.37 , Issue.2 , pp. 182-233
    • Jordan, M.I.1    Ghahramani, Z.2    Jaakkola, T.S.3    Saul, L.K.4
  • 11
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    • A view of the EM algorithm that justifies incremental, sparse, and other variants
    • Kluwer Academic Publishers
    • R. Neal and G. E. Hinton. A view of the EM algorithm that justifies incremental, sparse, and other variants. In Learning in Graphical Models, pages 355-368. Kluwer Academic Publishers, 1998.
    • (1998) Learning in Graphical Models , pp. 355-368
    • Neal, R.1    Hinton, G.E.2
  • 13
    • 10844237153 scopus 로고    scopus 로고
    • Lack of consistency of mean field and variational Bayes approximations for state space models
    • B. Wang and D. M. Titterington. Lack of consistency of mean field and variational Bayes approximations for state space models. Neural Processing Letters, 20(3):151-170, 2004.
    • (2004) Neural Processing Letters , vol.20 , Issue.3 , pp. 151-170
    • Wang, B.1    Titterington, D.M.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.