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Volumn , Issue , 2008, Pages

Sampling for approximate inference in continuous time Bayesian networks

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATE INFERENCE; CONTINUOUS TIME; EXPECTATION PROPAGATION; PARTICLE FILTERING; SAMPLING ALGORITHM; SMOOTHING ALGORITHMS; TIME EFFICIENCIES;

EID: 84864629090     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (44)

References (13)
  • 2
    • 0039223218 scopus 로고
    • Weighing and integrating evidence for stochastic simulation in Bayesian networks
    • Fung, R. M., and Chang, K.-C. 1989. Weighing and integrating evidence for stochastic simulation in Bayesian networks. In UAI, 209-220.
    • (1989) UAI , pp. 209-220
    • Fung, R.M.1    Chang, K.-C.2
  • 5
    • 78649424388 scopus 로고
    • Weighted average importance sampling and defensive mixture distributions
    • Hesterberg, T. 1995. Weighted average importance sampling and defensive mixture distributions. Technometrics 37(2):185-194.
    • (1995) Technometrics , vol.37 , Issue.2 , pp. 185-194
    • Hesterberg, T.1
  • 6
    • 0345978970 scopus 로고    scopus 로고
    • Expectation propagation for approximate Bayesian inference
    • Minka, T. P. 2001. Expectation propagation for approximate Bayesian inference. In UAI, 362-369.
    • (2001) UAI , pp. 362-369
    • Minka, T.P.1
  • 7
    • 84880748714 scopus 로고    scopus 로고
    • Continuous time particle filtering
    • Ng, B.; Pfeffer, A.; and Dearden, R. 2005. Continuous time particle filtering. In IJCAI.
    • (2005) IJCAI
    • Ng, B.1    Pfeffer, A.2    Dearden, R.3
  • 8
    • 78651414408 scopus 로고    scopus 로고
    • Continuous time Bayesian networks for inferring users' presence and activities with extensions for modeling and evaluation
    • Microsoft Research
    • Nodelman, U., and Horvitz, E. 2003. Continuous time Bayesian networks for inferring users' presence and activities with extensions for modeling and evaluation. Technical Report MSR-TR-2003-97, Microsoft Research.
    • (2003) Technical Report MSR-TR-2003-97
    • Nodelman, U.1    Horvitz, E.2
  • 9
    • 77956943618 scopus 로고    scopus 로고
    • Expectation propagation for continuous time Bayesian networks
    • Nodelman, U.; Koller, D.; and Shelton, C. R. 2005. Expectation propagation for continuous time Bayesian networks. In UAI, 431-440.
    • (2005) UAI , pp. 431-440
    • Nodelman, U.1    Koller, D.2    Shelton, C.R.3
  • 10
    • 77956508289 scopus 로고    scopus 로고
    • Continuous time Bayesian networks
    • Nodelman, U.; Shelton, C. R.; and Koller, D. 2002. Continuous time Bayesian networks. In UAI, 378-387.
    • (2002) UAI , pp. 378-387
    • Nodelman, U.1    Shelton, C.R.2    Koller, D.3
  • 11
    • 33749257956 scopus 로고    scopus 로고
    • Learning continuous time Bayesian networks
    • Nodelman, U.; Shelton, C. R.; and Koller, D. 2003. Learning continuous time Bayesian networks. In UAI, 451-458.
    • (2003) UAI , pp. 451-458
    • Nodelman, U.1    Shelton, C.R.2    Koller, D.3
  • 12
    • 80053214558 scopus 로고    scopus 로고
    • Reasoning at the right time granularity
    • Saria, S.; Nodelman, U.; and Koller, D. 2007. Reasoning at the right time granularity. In UAI.
    • (2007) UAI
    • Saria, S.1    Nodelman, U.2    Koller, D.3
  • 13
    • 85013513795 scopus 로고
    • Simulation approaches to general probabilistic inference on belief networks
    • Shachter, R. D., and Peot, M. A. 1989. Simulation approaches to general probabilistic inference on belief networks. In UAI, 221-234.
    • (1989) UAI , pp. 221-234
    • Shachter, R.D.1    Peot, M.A.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.