
›› 2012, Vol. 27 ›› Issue (1): 123.doi: 10.1007/s1139001212027
Special Issue: Artificial Intelligence and Pattern Recognition
• Artificial Intelligent and Pattern Recognition • Next Articles
Qin Zhang (张勤)
[1] Shortliffe E H, Buchanan B G. A model of inexact reason inmedicine. Mathematical Bioscience, 1975, 23(3/4): 351379. [2] Shafer G. A Mathematical Theory of Evidence. Princeton,NJ: Princeton University Press, 1976. [3] Duda R O et al. Development of the Prospector consultationsystem for mineral exploration. Final report, SRI Project5821 and 6415, SRI International, 1978. [4] Zadeh L A. The role of fuzzy logic in the management of uncertainty in expert systems. Fuzzy Sets and Systems, 1983,11: 199227. [5] Pearl J. Fusion, propagation, and structuring in belief networks. Artificial Intelligence, 1986, 29(3): 241288. [6] Pearl J. Probabilistic Reasoning in Intelligent Systems. SanMateo: Morgan Kaufmann, 1988. ISBN 0934613737. [7] Henrion M. Practical issues in constructing a Bayes' beliefnetwork. In Proc. the 3rd Conf. Uncertainty in ArtificialIntelligence, July 1987, pp.132139. [8] Srinivas S. A generalization of the noisyOR model. In Proc.the 9th Conf. Uncertainty in Artificial Intelligence, San Francisco, July 1993, pp.208215. [9] Diez F J. Parameter adjustment in Bayes networks: The generalized noisyOR gate. In Proc. the 9th Conf. Uncertaintyin Artificial Intelligence, 1993, pp.99105. [10] Pradhan M, Provan G, Middleton B, Henrion M. Knowledgeengineering for large belief networks. In Proc. the 10th Conference on Uncertainty in Artificial Intelligence, July1994, pp.484490. [11] Gilio A, Scozzafava R. Conditional events in probability assessment and revision. IEEE Trans. Systems, Man and Cybernetics, 1994, 24(12): 17411746. [12] Zhang N L, Poole D. A simple approach to Bayesian Networkcomputation. In Proc. the 10th Biennial Canadian ArtificialIntelligence Conference, 1994, pp.171178. [13] D'Ambrosio B. Local expression languages for probabilisticdependence. Int. J. Approximate Reasoning, 1995, 13(1):6168. [14] Boutilier C, Friedman N, Goldszmidt M, Koller D. Contextspecific independence in Bayesian network. In Proc. the12th Conf. Uncertainty in Artificial Intelligence, Aug. 1996,pp.115123. [15] Heckerman D, Breese J S. Causal independence for probability assessment and inference using Bayesian networks. IEEETrans. Systems, Man and Cybernetics, 1996, 26(6): 826831. [16] Cowell R G, Dawid A P, Lauritzen S L, Spiegelhalter DJ. Probabilistic Networks and Expert Systems. New York:Springer, 1999. [17] Takikawa M, D'Ambrosio B. Multiplicative factorization ofnoisyMAX. In Proc. the 15th Conf. Uncertainty in Artificial Intelligence, July 30Aug. 1, 1999, pp.622630. [18] Jensen F V. Bayesian graphical models. In Encyclopedia ofEnvironmetrics, Sussex: Wiley, 2000. [19] Pfeffer A. Sufficiency, separability and temporal probabilisticmodels. In Proc. the 17th Conf. Uncertainty in ArtificialIntelligence, Aug. 2001, pp.421428. [20] Russell S J, Norvig P. Artificial Intelligence: A Modern Approach, 2nd edition. Prentice Hall, 2003. [21] Pearl J, Russell S. Bayesian networks. In The Handbook ofBrain Theory and Neural Networks, 2nd edition, Arbib M A(ed.), MIT Press, 2003. [22] Poole D, Zhang N L. Exploiting contextual independence inprobabilistic inference. Journal of Artificial Intelligence Research, 2003, 18(1): 263313. [23] Zagorecki A, Druzdzel M. An empirical study of probabilityelicitation under noisyOR assumption. In Proc. the 17thFLAIRS, May 2004, pp.880885. [24] Schubert L K. A new characterization of probabilities inBayesian networks. In Proc. the 20th Conf. Uncertaintyin Artificial Intelligence, Banff, Canada, July 711, 2004,pp.495503. [25] Lucas P J F. Bayesian network modeling through qualitativepatterns. Artificial Intelligence, 2005, 163(2): 233263. [26] Chavira M, Allen D, Darwiche A. Exploiting evidence inprobabilistic inference. In Proc. the 21st Conf. Uncertaintyin Artificial Intelligence, July 2005, pp.112119. [27] Milch B, Marthi B, Sontag D, Russell S, Ong D L, Kolobov.Approximate inference for infinite contingent Bayesian networks. In Proc. the 10th International Workshop on Artificial Intelligence and Statistics, Barbados, Jan. 2005. [28] Zagorecki A, Voortman M, Druzdzel M J. Decomposing localprobability distributions in Bayesian networks for improvedinference and parameter learning. In Proc. the 19th FLAIRSConference, Melbourne Beach, USA, May 2006, pp.860865. [29] van Gerven M A J, Lucas P J F, van der Weide Th P. Ageneric qualitative characterization of independence of causalinfluence. International Journal of Approximate Reasoning,2008, 48(1): 214236. [30] Li W, Poupart P, van Beek P. Exploiting causal independenceusing weighted model counting. In Proc. the 23rd AAAI Conference on Artificial Intelligence, Chicago, USA, July 1317,2008, pp.337343. [31] Zhang Q. Probabilistic reasoning based on dynamic causalitytrees/diagrams. Reliability Engineering and System Safety,1994, 46(3): 209220. 
No related articles found! 

