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Volumn , Issue , 2005, Pages 959-962

On the use of particle filtering for maximum likelihood parameter estimation

Author keywords

[No Author keywords available]

Indexed keywords

EXPECTATION MAXIMIZATION; HIDDEN STATE; MODEL PARAMETERS; PARTICLE FILTERING; ROBUSTIFICATION; SAMPLE SIZES; SEQUENTIAL MONTE CARLO; SEQUENTIAL SIMULATION; SIGNAL AND IMAGE PROCESSING;

EID: 64349094892     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (16)

References (13)
  • 1
    • 0141603121 scopus 로고    scopus 로고
    • Recursive computation of smoothed functionals of hidden Markovian processes using a particle approximation
    • O. Cappé. Recursive computation of smoothed functionals of hidden Markovian processes using a particle approximation. Monte Carlo Methods Appl., 7(1-2):81-92, 2001.
    • (2001) Monte Carlo Methods Appl. , vol.7 , Issue.1-2 , pp. 81-92
    • Cappé, O.1
  • 3
    • 0002241694 scopus 로고
    • The SEM algorithm: A probabilistic teacher algorithm derived from the em algorithm for the mixture problem
    • G. Celeux and J. Diebolt. The SEM algorithm: a probabilistic teacher algorithm derived from the EM algorithm for the mixture problem. Comput. Statist., 2:73-82, 1985.
    • (1985) Comput. Statist. , vol.2 , pp. 73-82
    • Celeux, G.1    Diebolt, J.2
  • 5
    • 0036504051 scopus 로고    scopus 로고
    • A survey of convergence results on particle filtering methods for practitioners
    • D. Crisan and A. Doucet. A survey of convergence results on particle filtering methods for practitioners. IEEE Trans. Signal Process., 50(3):736-746, 2002.
    • (2002) IEEE Trans. Signal Process. , vol.50 , Issue.3 , pp. 736-746
    • Crisan, D.1    Doucet, A.2
  • 7
    • 0002629270 scopus 로고
    • Maximum likelihood from incomplete data via the em algorithm
    • with discussion
    • A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. Ser. B, 39(1):1-38 (with discussion), 1977.
    • (1977) J. Roy. Statist. Soc. Ser. B , vol.39 , Issue.1 , pp. 1-38
    • Dempster, A.P.1    Laird, N.M.2    Rubin, D.B.3
  • 9
    • 0001460136 scopus 로고    scopus 로고
    • On sequential Monte-Carlo sampling methods for Bayesian filtering
    • A. Doucet, S. Godsill, and C. Andrieu. On sequential Monte-Carlo sampling methods for Bayesian filtering. Stat. Comput., 10:197-208, 2000.
    • (2000) Stat. Comput. , vol.10 , pp. 197-208
    • Doucet, A.1    Godsill, S.2    Andrieu, C.3
  • 10
    • 0141803858 scopus 로고    scopus 로고
    • Parameter estimation in general state-space models using particle methods
    • A. Doucet and V. B. Tadić. Parameter estimation in general state-space models using particle methods. Ann. Inst. Statist. Math., 55(2):409-422, 2003.
    • (2003) Ann. Inst. Statist. Math. , vol.55 , Issue.2 , pp. 409-422
    • Doucet, A.1    Tadić, V.B.2
  • 11
    • 0032221057 scopus 로고    scopus 로고
    • Monte Carlo approximations for general state-space models
    • M. Hürzeler and H. R. Künsch. Monte Carlo approximations for general state-space models. J. Comput. Graph. Statist., 7:175-193, 1998.
    • (1998) J. Comput. Graph. Statist. , vol.7 , pp. 175-193
    • Hürzeler, M.1    Künsch, H.R.2
  • 12
    • 1542427941 scopus 로고    scopus 로고
    • Filtering via simulation: Auxiliary particle filters
    • M. K. Pitt and N. Shephard. Filtering via simulation: Auxiliary particle filters. J. Am. Statist. Assoc., 94(446):590-599, 1999.
    • (1999) J. Am. Statist. Assoc. , vol.94 , Issue.446 , pp. 590-599
    • Pitt, M.K.1    Shephard, N.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.