Learning Bayesian networks from data: An information-theory based approach

Artificial Intelligence - Tập 137 - Trang 43-90 - 2002
Jie Cheng1, Russell Greiner1, Jonathan Kelly1, David Bell2, Weiru Liu2
1Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8
2Faculty of Informatics, University of Ulster, UK

Tài liệu tham khảo

Acid, 1996, An algorithm for finding minimum d-separating sets in belief networks Acid, 1996, BENEDICT: An algorithm for learning probabilistic belief networks Agresti, 1990 Badsberg, 1992, Model search in contingency tables in CoCo, 251 Beinlich, 1989, The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks, 247 Buntine, 1994, Operations for learning with graphical models, J. Artificial Intelligence Res., 2, 159, 10.1613/jair.62 Buntine, 1996, A guide to the literature on learning probabilistic networks from data, IEEE Trans. Knowledge Data Engrg., 8, 195, 10.1109/69.494161 Cheng, 1997, An algorithm for Bayesian belief network construction from data, 83 Cheng, 1997, Learning belief networks from data: An information theory based approach Cheng, 1998 Cheng, 1999, Comparing Bayesian Network Classifiers Cheng, 2001, Learning Bayesian belief network classifiers: Algorithms and system Chickering, 1994 Chickering, 1996, Learning equivalence classes of Bayesian network structures Chow, 1968, Approximating discrete probability distributions with dependence trees, IEEE Trans. Inform. Theory, 14, 462, 10.1109/TIT.1968.1054142 Chrisman, 1996 Cooper, 1992, A Bayesian method for the induction of probabilistic networks from data, Machine Learning, 9, 309, 10.1007/BF00994110 Cowell, 2001, When learning Bayesian networks from data, using conditional independence tests is equivalent to a local scoring metric C. Darken, Personal communication Dash, 1999, A hybrid anytime algorithm for the construction of causal models from sparse data Edwards, 1995 Friedman, 1998, The Bayesian structural EM algorithm Friedman, 1997, Bayesian network classifiers, Machine Learning, 29, 131, 10.1023/A:1007465528199 Friedman, 1996, Learning Bayesian networks with local structure Fung, 1990, Constructor: A system for the induction of probabilistic models Greiner, 1996, Learning Bayesian Nets that perform well, 198 Greiner, 2001, Efficient reasoning, Comput. Surveys, 33, 1, 10.1145/375360.375363 Heckerman, 1995 Heckerman, 1995, Learning Bayesian networks: The combination of knowledge and statistical data, Machine Learning, 20, 197, 10.1007/BF00994016 Henrion, 1988, Propagating uncertainty in Bayesian networks by probabilistic logic sampling, 149 Herskovits, 1990, Kutato: An entropy-driven system for construction of probabilistic expert systems from databases Hojsgaard, 1994 Krause, 1996 Kullback, 1951, On information and sufficiency, Ann. Math. Statist., 22, 76, 10.1214/aoms/1177729694 Lam, 1994, Learning Bayesian belief networks: An approach based on the MDL principle, Comput. Intelligence, 10, 269, 10.1111/j.1467-8640.1994.tb00166.x Madigan, 1994, Model selection and accounting for model uncertainty in graphical models using Occam's window, J. Amer. Statist. Assoc., 89, 1535, 10.1080/01621459.1994.10476894 Madigan, 1994, Strategies for graphical model selection Meek, 1995, Strong completeness and faithfulness in Bayesian networks Page Pearl, 1988 Ramoni, 1996 Ramoni, 1997 Rebane, 1987, The recovery of causal poly-tree from statistical data Scheines, 1994 Singh, 1997, Learning Bayesian networks from incomplete data Singh, 1995, Construction of Bayesian network structures from data: A brief survey and an efficient algorithm, Internat. J. Approx. Reason., 12, 111, 10.1016/0888-613X(94)00016-V Spirtes, 1990, Causality from probability Spirtes, 1991, An algorithm for fast recovery of sparse causal graphs, Social Science Computer Review, 9, 62, 10.1177/089443939100900106 Spirtes, 1993 Spirtes, 1995, Learning Bayesian networks with discrete variables from data Spirtes, 1997, Heuristic greedy search algorithms for latent variable models, 481 Srinivas, 1990, Automated construction of sparse Bayesian networks from unstructured probabilistic models and domain information Suzuki, 1996, Learning Bayesian belief networks based on the MDL principle: An efficient algorithm using the branch and bound technique Thomas, 1992, BUGS: A program to perform Bayesian inference using Gibbs sampling, 837 Verma, 1990, Equivalence and synthesis of causal models Verma, 1992, An algorithm for deciding if a set of observed independencies has a causal explanation Wermuth, 1983, Graphical and recursive models for contingency tables, Biometrika, 72, 537, 10.2307/2336490 Wong, 1994, Construction of a Markov network from data for probabilistic inference, 562