参考文献

Akiba 94

Akiba, T. and Tanaka, H.: A Bayesian approach for user modelling in dialogue systems, Proc. of the International Conference on Computational Linguistics, pp. 1212-1218 (1994).

Breese 92

Breese, J.: Construction of Belief and Decision Networks, J. of Computational Intelligence, Vol. 8, No. 4, pp. 624-647 (1992).

Buntine 91

Buntine, W.: Theory refinement on Bayesian networks, Proc. of the 7th Conference on Uncertainty in Artificial Intelligence, pp. 52-60 (1991).

Castillo 97

Castillo, E., Gutierrez, J., and Hadi, A.: Expert Systems and Probabilistic Network Models, Springer-Verlag (1997).

Charniak 93

Charniak, E. and Goldman, R.: A Bayesian model of plan recognition, Artificial Intelligece, Vol. 64, pp. 53-79 (1993).

Cooper 90

Cooper, G. F.: The Computational Complexity of Probabilistic Inference using Bayesian Belief Networks, Artificial Intelligence, Vol. 42, pp. 393-405 (1990).

Cooper 92

Cooper, G. and Herskovits, E.: A Bayesian method for the induction of probabilistic networks from Data, Machine Learning, Vol. 9, pp. 309-347 (1992).

Cowell 99

Cowell, R. G., Dawid, A. P., Lauritzen, S. L., and Spiegelhalter, D. J.: Probabilistic Networks and Expert Systems, Springer-Verlag (1999).

Dean 89

Dean, T. and Kanazawa, K.: A model for Reasoning about Persistence and Causation, Computational Intelligence, Vol. 5, No. 3, pp. 142-150 (1989).

Dean 91

Dean, T. and Wellman, P.: Planning and Control, Morgan Kaufmann (1991).

Dempster 77

Dempster, A., Laird, N., and Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm, Journal of Royal Statististical Society B, Vol. 39, pp. 1-38 (1977).

Forbes 93

Forbes, J., Huang, T., Kanazawa, K., and Russel, S.: The BATmobile: Towards a Bayesian Automated Taxi, Proc. of the 14th International Joint Conference on Artificial Intelligence, pp. 1878-1885 (1993).

Friedman 97

Friedman, N., Goldszmidt, M., Heckerman, D., and Russell, S.: Challenge: Where is the Impact of Bayesian Networks in Learning?, Proc. of the 15th International Joint Conference on Artificial Intelligence, pp. 10-15 (1997).

Geiger 93

Geiger, D. and Heckerman, D.: Inference Algorithms for Similarity Networks, Proc. of the 9th Conference on Uncertainty in Artificial Intelligence, pp. 326-334 (1993).

Geiger 94

Geiger, D. and Heckerman, D.: Learning Gaussian Networks, Proc. of the 10th Conference on Uncertainty in Artificial Intelligence, pp. 235-243 (1994).

Geiger 95

Geiger, D. and Heckerman, D.: A characterization of the Dirichlet distribution with application to learning Bayesian networks, Proc. of the 11th Conference on Uncertainty in Artificial Intelligence, pp. 196-207 (1995).

Haddawy 99

Haddawy, P.: An Overview of Some Recent Developments in Bayesian Problem Solving Techniques, AI Magazine special issue on Bayesian Techniques Plus..., Vol. 20, No. 2, pp. 11-20 (1999).

Heckerman 95

Heckerman, D., Geiger, D., and Chickering, D.: Learning Bayesian networks: the combination of knowledge and statistical data, Machine Learning, Vol. 20, pp. 197-243 (1995).

Horvitz 98

Horvitz, E., Breese, J., Heckerman, D., Hovel, D., and Rommelse, K.: The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users, Proc. of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 256-265 (1998).

Jensen 96

Jensen, F.: An Introduction to Bayesian Networks, University College London Press (1996).

Jordan 98

Jordan, M., Ghahramani, Z., Jaakkola, T., and Saul, L.: An Introduction to Variational Methods for Graphical Models, Learning in Graphical Models, pp. 105-161 (1998), Kruwer Academic Publisher.

Kabashima 99

Kabashima, Y. and Saad, D.: Belief Propagetion vs. TAP for decoding corrupted messages, Europhys. Letter, Vol. 44, No. 5, pp. 668-674 (1999).

Kappen 99

Kappen, H. J. and al., et : Approximate inference in medical diagnosis, Pattern Recognition Letters (1999).

Koller 97

Koller, D. and Pfeffer, A.: Learning probabilities for noisy first-order rules, Proc.of IJCAI'97, Nagoya, pp. 1316-1321 (1997).

Larranaga 96

Larranaga, P., Poza, M., Yurramendi, Y., Murfa, R., and Kujipers, C.: Structure learning of Bayesian networks by genetic algorithms: a performance analysis of control parameters, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 18, pp. 912-926 (1996).

Lauritzen 88

Lauritzen, S. and Spiegelhalter, D.: Local computations with probabilities on graphical structures and their application to expert systems, Journal of the Royal Statistical Society B, Vol. 50, pp. 157-224 (1988).

Lauritzen 89

Lauritzen, S. and Wermuth, N.: Graphical Models for Associations Between Variables, Some of which are Qualitative and Some Quantitative, Anals. of Statistics, Vol. 17, pp. 31-57 (1989).

Mackay 96

Mackay, D.: Turbo Decoding as an Instance of Pearl's Belief Propagation Algorithm, submitted to IEEE Journal on Selected Areas in Communication (1996).

Motomura 00

Motomura, Y. and Hara, I.: Bayesian Network Learning System based on Neural Networks, to appear in the proc. of int. symp. on Theory and Application of Softcomputing 2000 (2000).

Paass 88

Paass, G.: Probabilistic Logic, Non-Standard Logics for Automated Reasoning (eds. Smets,P., Mamdani,A., Dubois,D. and Prade,H.) (1988), Academic Press.

Pearl 88

Pearl, J.: Probabilistic Reasoning in Intelligent Systems, Morgan Kaufmann, CA (1988).

Poole 93

Poole, D.: Probabilistic Horn abduction and Bayesian networks, Artificial Intelligence, Vol. 64, pp. 81-129 (1993).

Rabiner 93

Rabiner, L. and Juang, B.: Foundations of Speech Recognition, Prentice-Hall (1993).

Russell 95

Russell, S. and Norvig, P.: Artificial Intelligence, A modern approach, Prentice Hall (1995), 邦訳: 古川康一監訳，エージェントアプローチ 人工知能，共立出版 (1997).

Sato 00

Sato, T. and Kameya, Y.: A Viterbi-like algorithm and EM learning for statistical abductuion, to be presented at the UAI-2000 workshop on Fusion of Domain Knowledge with Data for Decision Support (2000).

Spiegelhalter 93

Spiegelhalter, D. J., Lauritzen, S. L., Dawid, A. P., and Cowell, R. G.: Bayesian analysis in expert systems, Statistical Science, Vol. 8, pp. 219-247 (1993).

Suzuki 93

Suzuki, J.: A construction of Bayesian networks from databases based on an MDL principle, in Proc. of the 9th Conference on Uncertainty in Artificial Intelligence, pp. 266-273 (1993).

Tanaka 00

Tanaka, T.: A theory of Mean Field Approximation, Advances in Neural Information Processing Systems, Vol. 11, pp. 351-357 (2000), MIT Press.

Thiesson 98

Thiesson, B., Meek, C., Chickering, D., and Heckerman, D.: Learning Mixtures of DAG Models, in Proc. of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 504-513 (1998).

Whittaker 90

Whittaker, J.: Graphical Models in Applied Multivariate Statistics, John Wiley and Sons (1990).

Wiegerinck 98

Wiegerinck, W. and Barber, D.: Mean Field Theory based on Belief Networks for Approximate Inference, Proc. of the International Conference on Artificial Neural Networks, pp. 499-504 (1998).

乾 97

乾, 徳永, 田中：意志決定理論に基づく 発話プランニング, 人工知能学会論文誌, Vol. 12, No. 5, pp. 760-769 (1997).

宮川 98

宮川雅巳：グラフィカルモデリング, 朝倉書店 (1998).

市川 00

市川誠(ゲストエディタ)：特集:21世紀の玩具とロボティクス, ロボット学会誌, Vol. 18, pp. 1-50 (2000).

石塚 97

石塚満(訳)：15章: 確率的推論システム, 古川康一監訳，エージェントアプローチ 人工知能, pp. 439-473 (1997), 共立出版.

本村,佐藤 00

本村, 佐藤：ベイジアンネットワーク-不確定性のモデリング技術-, 人工知能学会論文誌, vol.15, No.4, pp. 575-582, (2000).

本村 00

本村陽一：ベイジアンネットワーク, 電子情報通信学会誌, vol.83, No.8, pp. 645-646, (2000).