메뉴 건너뛰기




Volumn 2, Issue , 2003, Pages 83-91

Partial abductive inference in Bayesian networks by using probability trees

Author keywords

Abductive inference; Approximate propagation; Bayesian networks; Junction join trees; Probability trees

Indexed keywords

BAYESIAN NETWORKS; COMPLEX NETWORKS; FORESTRY; INFORMATION SYSTEMS; PROBABILITY; TREES (MATHEMATICS);

EID: 84910071893     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (3)

References (23)
  • 6
    • 0025401005 scopus 로고
    • Probabilistic inference using belief networks is NP-hard
    • Cooper, G. (1990). Probabilistic inference using belief networks is NP-hard. Artificial Intelligence, pages 393- 405.
    • (1990) Artificial Intelligence , pp. 393-405
    • Cooper, G.1
  • 7
    • 0003064380 scopus 로고
    • Applications of a general propagation algorithm for probabilistic expert systems
    • Dawid, A. (1992). Applications of a general propagation algorithm for probabilistic expert systems. Statistics and Computing, 2:25-36.
    • (1992) Statistics and Computing , vol.2 , pp. 25-36
    • Dawid, A.1
  • 9
    • 26944484635 scopus 로고    scopus 로고
    • On the problem of performing exact partial abductive inference in bayesian belief networks using junction trees
    • Bouchon-Meunier, B. Gutierrez-Rios, J. Magdalena, L. and Yager, R. editors, Physica-Verlag
    • de Campos, L., Gámez, J., and Moral, S. (2002a). On the problem of performing exact partial abductive inference in Bayesian belief networks using junction trees. In Bouchon-Meunier, B., Gutierrez-Rios, J., Magdalena, L., and Yager, R., editors, Technologies for Constructing Intelligent Systems 2: Tools, pages 289-302. Physica-Verlag.
    • (2002) Technologies for Constructing Intelligent Systems 2: Tools , pp. 289-302
    • De Campos, L.1    Gámez, J.2    Moral, S.3
  • 10
    • 0036531188 scopus 로고    scopus 로고
    • Partial abductive inference in bayesian belief networks: An evolutionary computation approach by using problem specific genetic operators
    • de Campos, L., Gámez, J., and Moral, S. (2002b). Partial Abductive Inference in Bayesian Belief Networks: An Evolutionary Computation Approach by Using Problem Specific Genetic Operators. IEEE Transaction on Evolutionary Computation, (6):105-131.
    • (2002) IEEE Transaction on Evolutionary Computation , Issue.6 , pp. 105-131
    • De Campos, L.1    Gámez, J.2    Moral, S.3
  • 11
    • 0342902142 scopus 로고    scopus 로고
    • Partial abductive inference in bayesian belief networks by using simulated annealing
    • de Campos, L., Gámez, J., and Moral, S. (2002c). Partial Abductive Inference in Bayesian Belief Networks by using Simulated Annealing. International Journal of Approximate Reasoning, (27):263-283.
    • (2002) International Journal of Approximate Reasoning , Issue.27 , pp. 263-283
    • De Campos, L.1    Gámez, J.2    Moral, S.3
  • 12
    • 8344276113 scopus 로고    scopus 로고
    • Elvira: An environment for creating and using probabilistic graphical models
    • Elvira Consortium . Gámez, J. and Salmerón, A. editors
    • Elvira Consortium (2002). Elvira: An environment for creating and using probabilistic graphical models. In Gámez, J. and Salmerón, A., editors, Proceedings of the First European Workshop on Probabilistic Graphical Models (PGM'02), pages 222-230.
    • (2002) Proceedings of the First European Workshop on Probabilistic Graphical Models (PGM'02) , pp. 222-230
  • 14
    • 0004107433 scopus 로고
    • Probabilistic reasoning in expert systems
    • Wiley Interscience
    • Neapolitan, R. E. (1990). Probabilistic Reasoning in Expert Systems. Theory and Algorithms. Wiley Interscience.
    • (1990) Theory and Algorithms
    • Neapolitan, R.E.1
  • 16
    • 0006776658 scopus 로고    scopus 로고
    • An efficient algorithm for finding the M most probable configurations in bayesian networks
    • Nilsson, D. (1998). An efficient algorithm for finding the M most probable configurations in Bayesian networks. Statistics and Computing, 9:159-173.
    • (1998) Statistics and Computing , vol.9 , pp. 159-173
    • Nilsson, D.1
  • 19
    • 33744584654 scopus 로고
    • Induction of decision trees
    • Quinlan, J. (1986). Induction of decision trees. Machine Learning, 1:81-106.
    • (1986) Machine Learning , vol.1 , pp. 81-106
    • Quinlan, J.1
  • 21
    • 0028516334 scopus 로고
    • An algorithm directly finding the K most probable configurations in bayesian networks
    • Seroussi, B. and Goldmard, J. (1994). An algorithm directly finding the K most probable configurations in Bayesian networks. International Journal of Approximate Reasoning, 11:205-233.
    • (1994) International Journal of Approximate Reasoning , vol.11 , pp. 205-233
    • Seroussi, B.1    Goldmard, J.2
  • 22
    • 0028483915 scopus 로고
    • Finding maps for belief networks is NP-hard
    • Shimony, S. (1994). Finding maps for belief networks is NP-hard. Artificial Intelligence, 68:399-410.
    • (1994) Artificial Intelligence , vol.68 , pp. 399-410
    • Shimony, S.1
  • 23
    • 0029272913 scopus 로고
    • Computing marginals for arbitrary subsets from marginal representation in markov trees
    • Xu, H. (1995). Computing marginals for arbitrary subsets from marginal representation in markov trees. Artificial Intelligence, 74:177-189.
    • (1995) Artificial Intelligence , vol.74 , pp. 177-189
    • Xu, H.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.