메뉴 건너뛰기




Volumn 20, Issue 2, 1999, Pages 21-36

Inference in Bayesian networks

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL COMPLEXITY; COMPUTER SIMULATION; DECISION THEORY; GRAPH THEORY; HEURISTIC METHODS; MARKOV PROCESSES; PROBABILITY DENSITY FUNCTION; TREES (MATHEMATICS);

EID: 0032668348     PISSN: 07384602     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (61)

References (59)
  • 7
    • 0344819767 scopus 로고
    • Node aggregation for distributed inference in Bayesian networks
    • Menlo Park, Calif.: International Joint Conferences on Artificial Intelligence
    • Chang, K. C., and Fung, R. M. 1989. Node Aggregation for Distributed Inference in Bayesian Networks. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, 265-270. Menlo Park, Calif.: International Joint Conferences on Artificial Intelligence.
    • (1989) Proceedings of the Eleventh International Joint Conference on Artificial Intelligence , pp. 265-270
    • Chang, K.C.1    Fung, R.M.2
  • 8
    • 0025401005 scopus 로고
    • The computational complexity of probabilistic inference using Bayesian belief networks
    • Cooper, G. 1990. The Computational Complexity of Probabilistic Inference Using Bayesian Belief Networks. Artificial Intelligence 42(2-3): 393-406.
    • (1990) Artificial Intelligence , vol.42 , Issue.2-3 , pp. 393-406
    • Cooper, G.1
  • 9
    • 0008586604 scopus 로고
    • A method for using belief networks as influence diagrams
    • 19-21 August, Minneapolis, Minnesota
    • Cooper, G. 1988. A Method for Using Belief Networks as Influence Diagrams. Paper presented at the 1988 Workshop on Uncertainty in AI, 19-21 August, Minneapolis, Minnesota.
    • (1988) 1988 Workshop on Uncertainty in AI
    • Cooper, G.1
  • 10
    • 0027560587 scopus 로고
    • Approximating probabilistic inference in Bayesian belief networks is NP-hard
    • Dagum, P., and Luby, M. 1993. Approximating Probabilistic Inference in Bayesian Belief Networks Is NP-Hard. Artificial Intelligence 60(1): 141-153.
    • (1993) Artificial Intelligence , vol.60 , Issue.1 , pp. 141-153
    • Dagum, P.1    Luby, M.2
  • 11
    • 0006493176 scopus 로고
    • SPI in large BN2O networks
    • eds. D. Poole and R. Lopez de Mantaras, San Francisco, Calif.: Morgan Kaufmann
    • D'Ambrosio, B. 1994. SPI in Large BN2O Networks. In Tenth Annual Conference on Uncertainty on AI, eds. D. Poole and R. Lopez de Mantaras, 128-135. San Francisco, Calif.: Morgan Kaufmann.
    • (1994) Tenth Annual Conference on Uncertainty on AI , pp. 128-135
    • D'Ambrosio, B.1
  • 16
    • 84880665054 scopus 로고    scopus 로고
    • Mini-buckets: A general scheme for generating approximations in automated reasoning
    • Menlo Park, Calif.: International Joint Conferences on Artificial Intelligence
    • Dechter, R. 1997. Mini-Buckets: A General Scheme for Generating Approximations in Automated Reasoning. In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, 1297-1302. Menlo Park, Calif.: International Joint Conferences on Artificial Intelligence.
    • (1997) Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence , pp. 1297-1302
    • Dechter, R.1
  • 17
    • 0002251094 scopus 로고    scopus 로고
    • Bucket elimination: A unifying framework for probabilistic inference
    • eds. E. Horvitz and F. Jensen, San Francisco, Calif.: Morgan Kaufmann
    • Dechter, R. 1996. Bucket Elimination: A Unifying Framework for Probabilistic Inference. In Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence, eds. E. Horvitz and F. Jensen, 211-219. San Francisco, Calif.: Morgan Kaufmann.
    • (1996) Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence , pp. 211-219
    • Dechter, R.1
  • 22
    • 33845802880 scopus 로고
    • D-separation: From theorems to algorithms
    • 18-20 August, Windsor, Ontario, Canada
    • Geiger, D.; T. Verma, T.; and Pearl, J. 1989. D-Separation: From Theorems to Algorithms. Paper presented at the Fifth Workshop on Uncertainty in AI, 18-20 August, Windsor, Ontario, Canada.
    • (1989) Fifth Workshop on Uncertainty in AI
    • Geiger, D.1    Verma, T.T.2    Pearl, J.3
  • 24
    • 0242581929 scopus 로고
    • A tractable inference algorithm for diagnosing multiple diseases
    • 18-20 August, Windsor, Ontario, Canada
    • Heckerman, D. 1989. A Tractable Inference Algorithm for Diagnosing Multiple Diseases. Paper presented at the Fifth Conference on Uncertainty in AI, 18-20 August, Windsor, Ontario, Canada.
    • (1989) Fifth Conference on Uncertainty in AI
    • Heckerman, D.1
  • 25
  • 26
    • 85012775611 scopus 로고
    • Propagating uncertainty in Bayesian networks by probabilistic logic sampling
    • eds. J. Lemmer and L. Kanal, New York: Elsevier Science
    • Henrion, M. 1988. Propagating Uncertainty in Bayesian Networks by Probabilistic Logic Sampling. In Uncertainty in Artificial Intelligence 2, eds. J. Lemmer and L. Kanal, 149-163. New York: Elsevier Science.
    • (1988) Uncertainty in Artificial Intelligence , vol.2 , pp. 149-163
    • Henrion, M.1
  • 27
    • 0006448878 scopus 로고
    • Bounded conditioning: Flexible inference for decisions under scarce resources
    • 18-20 August, Windsor, Ontario, Canada
    • Horvitz, E.; Suermondt, H. J.; and Cooper, G. 1989. Bounded Conditioning: Flexible Inference for Decisions under Scarce Resources. Paper presented at the Fifth Conference on Uncertainty in AI, 18-20 August, Windsor, Ontario, Canada.
    • (1989) Fifth Conference on Uncertainty in AI
    • Horvitz, E.1    Suermondt, H.J.2    Cooper, G.3
  • 30
    • 0001698979 scopus 로고
    • Bayesian updating in causal probabilistic networks by local computation
    • Jensen, F.; Lauritzen, S.; and Diesen, K. 1990. Bayesian Updating in Causal Probabilistic Networks by Local Computation. Computational Statistics Quarterly 4:269-282.
    • (1990) Computational Statistics Quarterly , vol.4 , pp. 269-282
    • Jensen, F.1    Lauritzen, S.2    Diesen, K.3
  • 31
    • 84987047423 scopus 로고
    • An algebra of Bayesian belief universes for knowledge-based systems
    • Jensen, F.; Olesen, K.; and Andersen, S. 1990. An Algebra of Bayesian Belief Universes for Knowledge-Based Systems. Networks 20(5): 637-659.
    • (1990) Networks , vol.20 , Issue.5 , pp. 637-659
    • Jensen, F.1    Olesen, K.2    Andersen, S.3
  • 33
    • 0020918216 scopus 로고
    • A computational model for causal and diagnostic reasoning in inference engines
    • Menlo Park, Calif.: International Joint Conferences on Artificial Intelligence
    • Kim, J. H., and Pearl, J. 1983. A Computational Model for Causal and Diagnostic Reasoning in Inference Engines. In Proceedings of the Eighth International Joint Conference on Artificial Intelligence, 190-193. Menlo Park, Calif.: International Joint Conferences on Artificial Intelligence.
    • (1983) Proceedings of the Eighth International Joint Conference on Artificial Intelligence , pp. 190-193
    • Kim, J.H.1    Pearl, J.2
  • 35
    • 0038902662 scopus 로고
    • Reduction of computational complexity in Bayesian networks through removal of weak dependencies
    • San Francisco, Calif.: Morgan Kaufmann
    • Kjaerulf, U. 1994. Reduction of Computational Complexity in Bayesian Networks through Removal of Weak Dependencies. In Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, 374-382. San Francisco, Calif.: Morgan Kaufmann.
    • (1994) Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence , pp. 374-382
    • Kjaerulf, U.1
  • 37
    • 0001006209 scopus 로고
    • Local computations with probabilities on graphical structures and their application to expert systems
    • Lauritzen, S., and Spiegelhalter, D. 1988. Local Computations with Probabilities on Graphical Structures and Their Application to Expert Systems. Journal of the Royal Statistical Society B50(2): 157-224.
    • (1988) Journal of the Royal Statistical Society , vol.B50 , Issue.2 , pp. 157-224
    • Lauritzen, S.1    Spiegelhalter, D.2
  • 38
    • 0002666730 scopus 로고    scopus 로고
    • A comparison of Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer architectures for computing marginals of probability distributions
    • San Francisco, Calif.: Morgan Kaufmann
    • Lepar, V., and Shenoy, P. 1998. A Comparison of Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer Architectures for Computing Marginals of Probability Distributions. In Proceedings of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence, 328-337. San Francisco, Calif.: Morgan Kaufmann.
    • (1998) Proceedings of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence , pp. 328-337
    • Lepar, V.1    Shenoy, P.2
  • 39
    • 0028466297 scopus 로고
    • Efficient inference in Bayes's nets as a combinatorial optimization problem
    • Li, Z., and D'Ambrosio, B. 1994. Efficient Inference in Bayes's Nets as a Combinatorial Optimization Problem. International Journal of Approximate Reasoning 10(5).
    • (1994) International Journal of Approximate Reasoning , vol.10 , Issue.5
    • Li, Z.1    D'Ambrosio, B.2
  • 45
    • 0023440173 scopus 로고
    • Distributed revision of composite beliefs
    • Pearl, J. 1987a. Distributed Revision of Composite Beliefs. Artificial Intelligence 33(2): 173-216.
    • (1987) Artificial Intelligence , vol.33 , Issue.2 , pp. 173-216
    • Pearl, J.1
  • 46
    • 0023347981 scopus 로고
    • Evidential reasoning using stochastic simulation of causal models
    • Pearl, J. 1987b. Evidential Reasoning Using Stochastic Simulation of Causal Models. Artificial Intelligence 32(2): 245-258.
    • (1987) Artificial Intelligence , vol.32 , Issue.2 , pp. 245-258
    • Pearl, J.1
  • 47
    • 0026140542 scopus 로고
    • Fusion and propagation with multiple observations in belief networks
    • Peot, M. A. 1991. Fusion and Propagation with Multiple Observations in Belief Networks. Artificial Intelligence 48(3): 299-318.
    • (1991) Artificial Intelligence , vol.48 , Issue.3 , pp. 299-318
    • Peot, M.A.1
  • 49
    • 84880668827 scopus 로고    scopus 로고
    • Probabilistic partial evaluation: Exploiting rule structure in probabilistic inference
    • Menlo Park, Calif.: International Joint Conferences on Artificial Intelligence
    • Poole, D. 1997. Probabilistic Partial Evaluation: Exploiting Rule Structure in Probabilistic Inference. In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, 1284-1291. Menlo Park, Calif.: International Joint Conferences on Artificial Intelligence.
    • (1997) Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence , pp. 1284-1291
    • Poole, D.1
  • 51
    • 0345682550 scopus 로고
    • Search in Bayesian horn clause networks
    • 12-14 October, Rosario, Washington
    • Poole, D. 1992. Search in Bayesian Horn Clause Networks. Paper presented at the Third International Workshop on Principles of Diagnosis, 12-14 October, Rosario, Washington.
    • (1992) Third International Workshop on Principles of Diagnosis
    • Poole, D.1
  • 52
    • 0024038570 scopus 로고
    • Probabilistic inference and inference diagrams
    • Shachter, R. 1988. Probabilistic Inference and Inference Diagrams. Operations Research 36(6): 589-604.
    • (1988) Operations Research , vol.36 , Issue.6 , pp. 589-604
    • Shachter, R.1
  • 53
    • 0001203638 scopus 로고
    • Evidential reasoning using likelihood weighting
    • 18-20 August, Windsor, Ontario, Canada
    • Shachter, R., and Peot, M. 1989. Evidential Reasoning Using Likelihood Weighting. Paper presented at the Fifth Workshop on Uncertainty in Artificial Intelligence, 18-20 August, Windsor, Ontario, Canada.
    • (1989) Fifth Workshop on Uncertainty in Artificial Intelligence
    • Shachter, R.1    Peot, M.2
  • 56
    • 0025471633 scopus 로고
    • Fundamental concepts of qualitative probabilistic networks
    • Wellman, M. 1990. Fundamental Concepts of Qualitative Probabilistic Networks. Artificial Intelligence 44(3): 257-302.
    • (1990) Artificial Intelligence , vol.44 , Issue.3 , pp. 257-302
    • Wellman, M.1
  • 58
    • 0001783333 scopus 로고
    • Optimization of inter-subnet belief updating in multiply sections Bayesian networks
    • San Francisco, Calif.: Morgan Kaufmann
    • Xiang, Y. 1995. Optimization of Inter-Subnet Belief Updating in Multiply Sections Bayesian Networks. In Proceedings of the Eleventh Conference on Uncertainty in AI, 565-574. San Francisco, Calif.: Morgan Kaufmann.
    • (1995) Proceedings of the Eleventh Conference on Uncertainty in AI , pp. 565-574
    • Xiang, Y.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.