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1 1 Bayesian Network Softwares Yoichi Motomura National Institute of Advanced Industrial Science and Technology (AIST) keywords: Bayesian network, Probabilistic reasoning, Statistical learning, Intelligent systems, Data mining 1. (WWW ) WWW 60% 30% ( ) ( ) 2. (1) (2) (3) 3 (1) (2) (3) P ( = y = x1,x2,...)=p ( ) (1) (2) ( ) 1
2 a 2002 バッテリの古さ ( 推定可能 ) バッテリの容量 プラグの古さプラグの状態 ( 推定可能 ) カーステレオ ( 観測可能 ) 点火系 燃料系 セルモータ ガソリンの残量 ( 観測可能 ) FUELメータ 1 セルモータの音 ( 観測可能 ) エンジンがかからない時何がもっとも怪しいか? エンジンスタート ポイント : どの位悪いと動かなくなるかという可能性を確率的に定量化する 例 : プラグの状態が悪いときでもバッテリーが十分なら 60% 点火 : ( ) DNA [ 02] Hugin 80 (belief propagation) J.Pearl singly 1 connected multiply connected Junction tree [Jensen 96] Hugin Hugin Junction tree 1989 Hugin Expert Aalborg Hugin Junction tree Hugin [Jensen 96] (Windows ) Hugin API ( ) Hugin GUI WWW 2 Junction tree Hugin SPSS Clementine 2
3 3 Hugin Clementine Link Nokia Hewlett-Packard Hugin 3 2 BayesBuilder Hugin Nijmegen Bayes- Builder Windows WWW MSBNx Microsoft Microsoft research MSBNx(Microsoft Bayesian Network) Windows MSBNx API MSBNx WWW BayesNetToolbox BayesNetToolbox 5 California Berkeley MATLAB MATLAB GUI MATLAB K.Murphy murphyk/bayes/bnt.html C++ OpenBayes project WWW Belief Network PowerConstructor Alberta Jie Cheng Belief Network PowerConstructor 7 KDD Cup 2001 Task1 [Cheng 02] , Conditional Independence 200 Bayesian Classifier(Naive Bayes) BN classifier augmented Naive-Bayes murphyk/bayes/bayes.html jcheng/bnsoft.htm
4 a 2002 [Cheng 99] 4 2 BayesWare Discover BayesWare Discover Paola Sebastiani Marco Ramoni WWW 8 PowerConstructor Greedy 4 3 BAYONET BAYONET BAYONET JAVA 1996 [ 96] RWC [ 02] SQL ( ) GUI Wizard BAYONET [Motomura 97, Motomura 00b] ( ) 8 Y ニューラルネットの汎化能力を利用した欠損データの補完 ( 疎データへの対応 ) 連続分布 P(Y X) による近似で欠損データを補完 2 X P(Y X)=G(μ,σ) ( またはその Mixture) μ = f1(x) σ = f2(x) ニューラルネット f1 f2 を与えられたデータで学習 Y Ex. P1 0.3 P2 0.4 P3 : P4 P5 条件付確率表 P6 P(Y X) = p I j X ベイジアンネットの条件付確率 ( 2) JAVA TCP/IP API SQL count 9 Hugin BayesBuilder BAYONET Hugin BAYONET-real WWW API 9 Linux PostgreSQL 10
5 5 BAYONET の応用例 アプリケーション アドバイス型障害診断システム WWW 原因の予測 履歴 ユーザ 事例 サンプル ( データベース ) モデル頻度データから ( ファイル ) 条件付確率へ依存関係の強い変数を検出しネットワークを構築 BAYONET GUI モデルの検討知識モデル ( ベイジアンネット ) を事例データから構築する 3 BAYONET Hugin Hewlett-Packard Dynasty 5 1 Hugin Expert Hewlett-Packard R&D Systems for Automated Customer Support Operations(SACSO) HP [Jensen 01] NASA Intel, Nokia 11 SACSO Hugin API Dynasty 12 WWW baynet/fieldedsystems.html Lumière Project[Horvitz 98] MSBNx [ 02] Windows OutLook LookOut 1998 LookOut mixed-initiative interaction [Horvitz 99a] LookOut Windows OutLook LookOut LookOut OutLook ( ) ( ) LookOut Friday afternoon, next week, lunch OutLook
6 a 2002 LookOut 13 ( ) Bayesian Receptionist [Horvitz 99b] 6. [ 97, 00a, 01, 02] 14 ( ) Lisp K.Murphy WWW murphyk/bayes/bnsoft.html [ 02] :,, Vol. 17, No. 5 (2002). [ 02] :,, Vol. 17, No. 5 (2002). [ 02] : RWC,, Vol. 17, No. 2, pp (2002). [Cheng 99] Cheng, J. and R.Greiner, : Comparing Bayesian Network Classifiers, proceedings of the fifteenth conference on uncertainty in artificial intelligence (1999). [Cheng 02] Cheng, J., Hatzis, C., Hayashi, H., Krogel, M., Morishita, S., Page, D., and Sese, J.: KDDD cup 2001 report, ACM SIGKDD Explorations, Vol. 3, No. 2 (2002). [Horvitz 98] Horvitz, E., Breese, J., Heckerman, D., Hovel, D., and Rommelse, D.: The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users, in 14th National Conference on Uncertainty in Artificial Intelligence (1998). [Horvitz 99a] Horvitz, E.: Principles of Mixed-Initiative User Interfaces, in Proceedings of ACM SIGCHI Conference on Human Factors in Computing Systems (1999). [Horvitz 99b] Horvitz, E.: Uncertainty, Action, and Interaction: In Pursuit of Mixed-Initiative Computing, in Intelligent Systems, IEEE Computer Society (1999). [ 97], ( ):, 15 :, (1997). [Jensen 96] Jensen, F. V.: An Introduction to Bayesian Networks, University College London Press (1996). [Jensen 01] Jensen, F. V. and et.al., : The SACSO methodology for troubleshooting complex systems, Artificial Intelligence for Engineering Design, Analysis and Manufacturing (AIEDAM), Vol. 15, pp (2001). [ 96],,,, :, 4 Technical Report SIG-CII (1996). [Motomura 97] Motomura, Y. and et.al., : Bayesian Network that Learns Conditional Probabilities by Neural Networks, in Proc. of the Int. Conf. on Neural Information Processing and Intelligent Information Systems (1997). [ 00a], :,, Vol. 15, No. 4, pp (2000). [Motomura 00b] Motomura, Y. and Hara, I.: Bayesian Network Learning System based on Neural Networks, in Proceedings of International Symposium on Theory and Applications of Soft Computing (2000). [ 02] :, BN2002 (2002). [ 01] : ( ), (2001). 19YY MM DD
motomura.dvi
Abstract: ( ) ( ) ( ) 1. ( ) 2. (Bayesian network, Bayesnet, belief network) [1,2,3,4,5] [6,7,8,9,10] 0 1 1 0 X i,x j X i X j X j X i Pa(X3) X1 P(X3 X1,X2) X3 P(X5 X3,X4) X5 Pa(X4) X2 Pa(X5) X4 条件付確率表
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