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Volumn , Issue , 2000, Pages 596-602

Greedy importance sampling

Author keywords

[No Author keywords available]

Indexed keywords

MONTE CARLO METHODS;

EID: 84899001021     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (7)

References (15)
  • 1
    • 0027560587 scopus 로고
    • Approximating probabilistic inference in Bayesian belief networks is NP-hard
    • P. Dagum and M. Luby. Approximating probabilistic inference in Bayesian belief networks is NP-hard. Artif Intell, 60:141-153,1993.
    • (1993) Artif Intell , vol.60 , pp. 141-153
    • Dagum, P.1    Luby, M.2
  • 2
    • 0041709146 scopus 로고
    • Chaining via annealing
    • M. Evans. Chaining via annealing. Ann Statist, 19:382-393,1991.
    • (1991) Ann Statist , vol.19 , pp. 382-393
    • Evans, M.1
  • 4
    • 0001667705 scopus 로고
    • Baysian inference in econometric models using Monte Carlo integration
    • J. Geweke. Baysian inference in econometric models using Monte Carlo integration. Econometrica, 57:1317-1339,1989.
    • (1989) Econometrica , vol.57 , pp. 1317-1339
    • Geweke, J.1
  • 6
    • 0001635376 scopus 로고    scopus 로고
    • Coutour tracking by stochastic propagation of conditional density
    • M. Isard and A. Blake. Coutour tracking by stochastic propagation of conditional density. In ECCV, 1996.
    • (1996) ECCV
    • Isard, M.1    Blake, A.2
  • 8
    • 84898997495 scopus 로고
    • Stochastic simulation algorithms for dynamic probabilistic networks
    • K. Kanazawa, D. Roller, and S. Russell. Stochastic simulation algorithms for dynamic probabilistic networks. In VAI, 1995.
    • (1995) VAI
    • Kanazawa, K.1    Roller, D.2    Russell, S.3
  • 9
    • 84966254647 scopus 로고
    • Estimating the efficiency of backtracking algorithms
    • D. Knuth. Estimating the efficiency of backtracking algorithms. Math. Comput., 29(129):121-136,1975.
    • (1975) Math. Comput. , vol.29 , Issue.129 , pp. 121-136
    • Knuth, D.1
  • 14
    • 0001203638 scopus 로고
    • Simulation approaches to general probabilistic inference in belief networks
    • Elsevier
    • R. Shacter and M. Peot. Simulation approaches to general probabilistic inference in belief networks. In Uncertainty in Artificial Intelligence 5. Elsevier, 1990.
    • (1990) Uncertainty in Artificial Intelligence , vol.5
    • Shacter, R.1    Peot, M.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.