Data processing: artificial intelligence – Knowledge processing system
Reexamination Certificate
2008-01-29
2008-01-29
Starks, Jr., Wilbert L (Department: 2129)
Data processing: artificial intelligence
Knowledge processing system
C701S050000, C707S793000, C707S793000
Reexamination Certificate
active
10147620
ABSTRACT:
Methods and systems are disclosed for learning Bayesian networks. The approach is based on specifying a search space that enables searching over equivalence classes of the Bayesian network. A set of one or more operators are applied to a representation of the equivalence class. A suitable search algorithm searches in the search space by scoring the operators locally with a decomposable scoring criteria. To facilitate application of the operators and associated scoring, validity tests can be performed to determine whether a given operator is valid relative to the current state representation.
REFERENCES:
patent: 5832182 (1998-11-01), Zhang et al.
patent: 6221592 (2001-04-01), Schwartz et al.
patent: 6363161 (2002-03-01), Laumeyer et al.
patent: 6411952 (2002-06-01), Bharat et al.
patent: 6453056 (2002-09-01), Laumeyer et al.
patent: 6934615 (2005-08-01), Flann et al.
patent: 6947927 (2005-09-01), Chaudhuri et al.
Chickering, David Maxwell, Learning Equivalence Classes of Bayesian Network Structures, Proceedings of the Thirteenth National Conference on Uncertainty in Artificial Intelligence, 1996, Portland, OR, pp. 150-157, Morgan Kaufmann.
Chickering, David Maxwell, Learning Equivalence Classes of Bayesian-Network Structures, Journal of Machine Learning Research, vol. 2, pp. 445-498, Feb. 2002.
Paul Munteanu, et al., “Efficient Score-Based Learning of Equivalence Classes of Bayesian Networks”, PKDD 2000, LNAI 1910, pp. 96-105.
C. K. Chow, et al., Approximating Discrete Probability Distributions with Dependence Trees, IEEE Transactions on Information Theory, vol. IT-14, No. 3, May 1968, pp. 462-467.
Joe Suzuki, “An Extension of Learning Bayesian Belief Networks Based on MDL Principle”, Osaka University, p. 232.Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence, 1993.
David M. Chickering, et al., “Learning Bayesian Networks: Search Methods and Experimental Results”, 17 pages. InProceedings of the Fifth International Workshop on Artificial and Statistics, 1995.
Judea Pearl, et al., “A Theory of Inferred Causation”, This paper is a modified version of one presented at the Second International Conference conference on the Principles of Knowledge Representation and Reasoning, Apr. 1991, 12 pages.
Marek J. Druzdzel, et al., “Causality in Bayesian Belief Networks”, InProceedings of the Ninth Annual Conference on Uncertainty in Artificial Intelligence(UAI-93), pp. 3-11, Morgan Kaufman Publishers, Inc., 1993.
Dan Geiger, et al., “Stratified Exponential Families: Graphical Models and Model Selection”, Technical Report MSR-TR-98-31, Jul. 1998, pp. 1-30.
Gregory F. Cooper, et al., “A Bayesian Method for the Induction of Probabilistic Networks from Data”, Machine Learning, 9, 1992; pp. 309-347.
David Maxwell Chickering, “Learning Equivalence Classes of Bayesian Network Structures”, University of California at Los Angeles, Technical Report MSR-TR-2001-65, 2001.
Steven B. Gillispie, et al., “Enumerating Markov Equivalence Classes of Acyclic Digraph Models”, University of Washington, 7 pages,Proceedingsof the Seventeenth Conference on Uncertainty in Artificial Intelligenece, 2001.
Denver Dash, et al., “A Hybrid Anytime Algorithm for the Construction of Casual Models From Sparse Data”, InProceedings of the Fifteenth Annual Conference on Uncertainty in Artificial Intelligence(UAI-99), pp. 142-149, Morgan Kaufman Publishers, Inc., 1999.
Wray Buntine, “Theory Refinement on Bayesian Networks”, fromUncertainty in Artificial Intelligence, Proceedings of, 1991, pp. 52-60.
Remco R. Bouckaert, “Probalistic Network Construction Using the Minimum Description Length Principle”, Utrecht University, Jul. 1994; pp. 1-23.
David Heckerman, et al., “Decision-Theoretic Foundations for Causal Reasoning”, Journal of Artificial Intelligence Research 3 (1995), pp. 405-430.
Peter Spirtes, et al., “Learning Bayesian Networks with Discrete Variables from Data”, Carnegie Mellon University,Proceedings of the First International Conference on Knowledge Discovery and Data Mining, pp. 294-299, AAAI Press.
David Maxwell Chickering, “Learning Bayesian Networks is NP-Complete”, University of California at Los Angeles,Learning from Data: Artificial Intelligence and Statistics V, pp. 121-130. Springer-Verlag, 1996.
David Maxwell Chickering, “A Transformational Characterization of Equivalent Bayesian Network Structures”, University of California at Los Angeles, 18 pages,Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, 1995.
Tomás Kocka, et al., “On Inclusion Problem”, Feb. 12, 2001, pp. 1-38. Technical Report 2010, Academy of Sciences of the Czech Republic, Institute of Information Theory and Automation.
Chris Meek, “Causal Inference and Causal Explanation with Background Knowledge”, Dec. 23, 1997, pp. 1-19.
Ross D. Shachter, Bayes-Ball: The Rational Pastime (for Determining Irrelevance and Requisite Information in Belief Networks and Influence Diagrams), Stanford University, 9 pages. InProceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Aug. 1998.
Tomás Kocka, et al., “On Characterizing Inclusion of Bayesian Networks”,Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, pp. 261-268, 2001.
David Heckerman, et al., “Learning Bayesian Networks: The Combination of Knowledge and Statistical Data”,Machine LearningFeb. 1995, pp. 1-53.
Wray Buntine, “A Guide to the Literature on Learning Probabilistic Networks from Data”, IEEE, 8:195-210, 1996.
Steen A Andersson, et al., “A Characterization of Markov Equivalence Classes for Acyclic Digraphs”, Indiana University and University of Washington,Annals of Statistics, 25:505-541, 1997.
David Heckerman, et al., “A Tutorial on Learning With Bayesian Networks”, Technical Report MSR-TR-95-06, Mar. 1995 (Revised Nov. 1996) pp. 1-57.
David Maxwell Chickering, “Learning Equivalence Classes of Bayesian-Network Structures”, Journal of Machine Learning Research 2 (2002) pp. 445-498. Submitted Jul. 2001; Published Feb. 2002.
Judea Pearl, “Probablistic Reasoning in Intelligent Systems: Networks of Plausible Inference”, (revised second printing) 1988.
Sir Harold Jeffreys, “Theory of Probability” (third edition) 1961.
Peter Spirtes, et al., “Causation, Prediction, and Search” (search edition), 2000.
Michael I. Jordan, “Learning in Graphical Models”, 1998.
Amin Turocy & Calvin LLP
Microsoft Corporation
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