Probabilistic reasoning and multiple-expert methodology for correlated objective data

Artificial Intelligence in Engineering - Tập 12 - Trang 21-33 - 1998
Kwoh Chee-Keong1
1The Intelligent System Laboratory, School of Applied Science, Nanyang Technological University, Singapore 639798

Tài liệu tham khảo

Russell, 1995 Pearl, 1988 Anderson, 1989, HUGIN — a shell for building Bayesian belief universes for expert systems, Vol. 2, 1080 Heckerman, 1990, Probabilistic similarity networks, 10.1002/net.3230200508 Khan, 1989, Machine vision for endoscope control and navigation Khan, 1988, A highly parallel shade image segmentation method Khan, 1992, Extracting contours by perceptual grouping, Image and Vision Computing, 10, 77, 10.1016/0262-8856(92)90002-K Rashid, 1991, Shape from shading and motion parameter estimation under the near light source illumination Sucar, 1992, Integrating shape from shading in a gradient histogram and its application to endoscope navigation Sucar, 1992, Expressing relational and temporal knowledge in visual probabilistic networks, 303 Keong, 1994, Using Fourier information for the detection of the lumen in endoscope images, 981 Sucar, 1991, Probabilistic reasoning in knowledge-based vision systems Provan, 1990, An analysis of knowledge representation schemes for high level vision, 537 Clark, 1990, Numerical and symbolic approaches to uncertainty management in AI, Artificial Intelligence Review, 4, 109, 10.1007/BF00133189 Keung-Chi, 1990, Uncertainty management in expert systems, IEEE Expert, 29 Neapolitan, 1990 Finn, 1992, Bayesian methods for interpretation and control in multi-agent vision systems, 1708, 536, 10.1117/12.58599 Madigan, 1994, Model selection and accounting for model uncertainty in graphical models using Occam's window, Journal of the American Statistical Association, 89, 1535, 10.2307/2291017 Spiegelhalter, 1993, Bayesian analysis in expert systems, Statistical Science, 8, 219, 10.1214/ss/1177010888 Spiegelhalter, 1994, Empirical evaluation of prior beliefs about frequencies: methodology and a case study in congenital heart disease, Journal of the American Statistical Association, 89, 435, 10.2307/2290843 Ibarguengoytia, 1995, Real time intelligent signal validation in power plants Sucar, 1995, Induction of dependency structures from data and its application to ozone predication Montgomery, 1976 Rebane, 1989, The recovery of causal poly-trees from statistical data, 3, 175 Geiger, 1992, An entropy-based learning algorithm of Bayesian conditional trees, 92 Cooper, 1991, A Bayesian method for constructing Bayesian belief networks for databases, 86 Cooper, 1992, A Bayesian method for the induction of probabilistic networks from data, Machine Learning, 9, 309, 10.1007/BF00994110 Molina, 1993, Using Bayesian algorithms for learning causal networks in classification problems, 49 Chow, 1986, Approximating discrete probability distributions with dependence trees, IEEE Transactions on Information Theory, 14, 462, 10.1109/TIT.1968.1054142 Kwoh Chee Keong, 1995, Probabilistic reasoning from correlated objective data Sucar, 1993, Objective probabilities in expert systems, Artificial Intelligence, 61, 187, 10.1016/0004-3702(93)90067-L Kwoh, Chee Keong & Gillies, D. F., Using hidden nodes in Bayesian networks. Artificial Intelligence Journal (in press). Kwoh, Chee Keong, Ismaili, I. A. & Gillies, D. F., On the use of orthogonal transformations in probabilistic inference systems. SIAM Journal on Computing (submitted). Madigan, D. & York, J., Bayesian graphical models for discrete data. International Statistical Reivew, 1993 (in press).