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Note: Links lead to the DBLP on the Web. David Heckerman Vladimir Jojic , Nebojsa Jojic , Christopher Meek , Dan Geiger , Adam C. Siepel , David Haussler , David Heckerman: Efficient approximations for learning phylogenetic HMM models from data. ISMB/ECCB (Supplement of Bioinformatics) 2004 : 161-168 David Heckerman: Graphical models for data mining. KDD 2004 : 2 David Maxwell Chickering , David Heckerman, Christopher Meek : Large-Sample Learning of Bayesian Networks is NP-Hard. Journal of Machine Learning Research 5 : 1287-1330 (2004) Ronen I. Brafman , David Heckerman, Guy Shani : Recommendation as a Stochastic Sequential Decision Problem. ICAPS 2003 : 164-173 David Maxwell Chickering , Christopher Meek , David Heckerman: Large-Sample Learning of Bayesian Networks is NP-Hard. UAI 2003 : 124-133 Igor V. Cadez , David Heckerman, Christopher Meek , Padhraic Smyth , Steven White : Model-Based Clustering and Visualization of Navigation Patterns on a Web Site. Data Min. Knowl. Discov. 7 (4): 399-424 (2003) Christopher Meek , David Maxwell Chickering , David Heckerman: Autoregressive Tree Models for Time-Series Analysis. SDM 2002 Carl Myers Kadie , Christopher Meek , David Heckerman: CFW: A Collaborative Filtering System Using Posteriors over Weights of Evidence. UAI 2002 : 242-250 Christopher Meek , Bo Thiesson , David Heckerman: Staged Mixture Modelling and Boosting. UAI 2002 : 335-343 Guy Shani , Ronen I. Brafman , David Heckerman: An MDP-based Recommender System. UAI 2002 : 453-460 Christopher Meek , Bo Thiesson , David Heckerman: The Learning-Curve Sampling Method Applied to Model-Based Clustering. Journal of Machine Learning Research 2 : 397-418 (2002) Nebojsa Jojic , Patrice Simard , Brendan J. Frey , David Heckerman: Separating Appearance from Deformation. ICCV 2001 : 288-294 Paolo Giudici , David Heckerman, Joe Whittaker : Statistical Models for Data Mining. Data Min. Knowl. Discov. 5 (3): 163-165 (2001) Marina Meila , David Heckerman: An Experimental Comparison of Model-Based Clustering Methods. Machine Learning 42 (1/2): 9-29 (2001) Bo Thiesson , Christopher Meek , David Heckerman: Accelerating EM for Large Databases. Machine Learning 45 (3): 279-299 (2001) David Maxwell Chickering , David Heckerman: Targeted advertising with inventory management. ACM Conference on Electronic Commerce 2000 : 145-149 Igor V. Cadez , David Heckerman, Christopher Meek , Padhraic Smyth , Steven White : Visualization of navigation patterns on a Web site using model-based clustering. KDD 2000 : 280-284 David Heckerman, David Maxwell Chickering , Christopher Meek , Robert Rounthwaite , Carl Myers Kadie : Dependency Networks for Collaborative Filtering and Data Visualization. UAI 2000 : 264-273 David Maxwell Chickering , David Heckerman: A Decision Theoretic Approach to Targeted Advertising. UAI 2000 : 82-88 David Heckerman, David Maxwell Chickering , Christopher Meek , Robert Rounthwaite , Carl Myers Kadie : Dependency Networks for Inference, Collaborative Filtering, and Data Visualization. Journal of Machine Learning Research 1 : 49-75 (2000) David Maxwell Chickering , David Heckerman: Fast Learning from Sparse Data. UAI 1999 : 109-115 David Heckerman, Eric Horvitz : Inferring Informational Goals from Free-Text Queries: A Bayesian Approach. UAI 1998 : 230-237 Eric Horvitz , Jack S. Breese , David Heckerman, David Hovel , Koos Rommelse : The Lumière Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users. UAI 1998 : 256-265 Marina Meila , David Heckerman: An Experimental Comparison of Several Clustering and Initialization Methods. UAI 1998 : 386-395 John S. Breese , David Heckerman, Carl Myers Kadie : Empirical Analysis of Predictive Algorithms for Collaborative Filtering. UAI 1998 : 43-52 Bo Thiesson , Christopher Meek , David Maxwell Chickering , David Heckerman: Learning Mixtures of DAG Models. UAI 1998 : 504-513 Dan Geiger , David Heckerman: Probabilistic relevance relations. IEEE Transactions on Systems, Man, and Cybernetic Part A 28 (1): 17-25 (1998) David Heckerman, Heikki Mannila , Daryl Pregibon : Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97), Newport Beach, California, USA, August 14-17, 1997 AAAI Press 1997 Nir Friedman , Moisés Goldszmidt , David Heckerman, Stuart J. Russell : Challenge: What is the Impact of Bayesian Networks on Learning? IJCAI (1) 1997 : 10-15 David Heckerman, Christopher Meek : Models and Selection Criteria for Regression and Classification. UAI 1997 : 223-228 Christopher Meek , David Heckerman: Structure and Parameter Learning for Causal Independence and Causal Interaction Models. UAI 1997 : 366-375 David Maxwell Chickering , David Heckerman, Christopher Meek : A Bayesian Approach to Learning Bayesian Networks with Local Structure. UAI 1997 : 80-89 David Heckerman: Bayesian Networks for Data Mining. Data Min. Knowl. Discov. 1 (1): 79-119 (1997) David Maxwell Chickering , David Heckerman: Efficient Approximations for the Marginal Likelihood of Bayesian Networks with Hidden Variables. Machine Learning 29 (2-3): 181-212 (1997) Padhraic Smyth , David Heckerman, Michael I. Jordan : Probabilistic Independence Networks for Hidden Markov Probability Models. Neural Computation 9 (2): 227-269 (1997) John S. Breese , David Heckerman: Decision-Theoretic Troubleshooting: A Framework for Repair and Experiment. UAI 1996 : 124-132 David Maxwell Chickering , David Heckerman: Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network. UAI 1996 : 158-168 Dan Geiger , David Heckerman, Christopher Meek : Asymptotic Model Selection for Directed Networks with Hidden Variables. UAI 1996 : 283-290 David Heckerman: Bayesian Networks for Knowledge Discovery. Advances in Knowledge Discovery and Data Mining 1996 : 273-305 Max Henrion , Henri Jacques Suermondt , David Heckerman: Probabilistic and Bayesian Representations of Uncertainty in Information Systems: A Pragmatic Introduction. Uncertainty Management in Information Systems 1996 : 255-284 Dan Geiger , David Heckerman: Knowledge Representation and Inference in Similarity Networks and Bayesian Multinets. Artif. Intell. 82 (1-2): 45-74 (1996) David Heckerman: Learning With Bayesian Networks (Abstract). ICML 1995 : 588 Dan Geiger , David Heckerman: A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks. UAI 1995 : 196-207 David Heckerman, Ross D. Shachter : A Definition and Graphical Representation for Causality. UAI 1995 : 262-273 David Heckerman, Dan Geiger : Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains. UAI 1995 : 274-284 David Heckerman: A Bayesian Approach to Learning Causal Networks. UAI 1995 : 285-295 David Heckerman, E. H. Mamdani , Michael P. Wellman : Real-World Applications of Bayesian Networks - Introduction. Commun. ACM 38 (3): 24-26 (1995) David Heckerman, Michael P. Wellman : Bayesian Networks. Commun. ACM 38 (3): 27-30 (1995) David Heckerman, John S. Breese , Koos Rommelse : Decision-Theoretic Troubleshooting. Commun. ACM 38 (3): 49-57 (1995) David Maxwell Chickering , Dan Geiger , David Heckerman: On Finding a Cycle Basis with a Shortest Maximal Cycle. Inf. Process. Lett. 54 (1): 55-58 (1995) David Heckerman, E. H. Mamdani , Michael P. Wellman : Editorial: real-world applications of uncertain reasoning. Int. J. Hum.-Comput. Stud. 42 (6): 573-574 (1995) David Heckerman, Ross D. Shachter : Decision-Theoretic Foundations for Causal Reasoning. J. Artif. Intell. Res. (JAIR) 3 : 405-430 (1995) David Heckerman, Dan Geiger , David Maxwell Chickering : Learning Bayesian Networks: The Combination of Knowledge and Statistical Data. Machine Learning 20 (3): 197-243 (1995) David Heckerman, Dan Geiger , David Maxwell Chickering : Learning Bayesian Networks: The Combination of Knowledge and Statistical Data. KDD Workshop 1994 : 85-96 Dan Geiger , David Heckerman: Learning Gaussian Networks. UAI 1994 : 235-243 David Heckerman, John S. Breese : A New Look at Causal Independence. UAI 1994 : 286-292 David Heckerman, Dan Geiger , David Maxwell Chickering : Learning Bayesian Networks: The Combination of Knowledge and Statistical Data. UAI 1994 : 293-301 David Heckerman, Ross D. Shachter : A Decision-based View of Causality. UAI 1994 : 302-310 David Heckerman, E. H. Mamdani : UAI '93: Proceedings of the Ninth Annual Conference on Uncertainty in Artificial Intelligence, July 9-11, 1993, The Catholic University of America, Providence, Washington, DC, USA Morgan Kaufmann 1993 David Heckerman: Causal Independence for Knowledge Acquisition and Inference. UAI 1993 : 122-127 Dan Geiger , David Heckerman: Inference Algorithms for Similarity Networks. UAI 1993 : 326-334 David Heckerman, Michael Shwe : Diagnosis of Multiple Faults: A Sensitivity Analysis. UAI 1993 : 80-90 David Heckerman, Eric Horvitz , Blackford Middleton : An Approximate Nonmyopic Computation for Value of Information. IEEE Trans. Pattern Anal. Mach. Intell. 15 (3): 292-298 (1993) Dan Geiger , David Heckerman: Advances in Probabilistic Reasoning. UAI 1991 : 118-126 David Heckerman, Eric Horvitz , Blackford Middleton : An Approximate Nonmyopic Computation for Value of Information. UAI 1991 : 135-141 David Heckerman, Eric Horvitz : Problem formulation as the reduction of a decision model. UAI 1990 : 159-170 Henri Jacques Suermondt , Gregory F. Cooper , David Heckerman: A combination of cutset conditioning with clique-tree propagation in the Pathfinder system. UAI 1990 : 245-254 David Heckerman: Similarity networks for the construction of multiple-faults belief networks. UAI 1990 : 51-64 Dan Geiger , David Heckerman: separable and transitive graphoids. UAI 1990 : 65-76 Eric Horvitz , Gregory F. Cooper , David Heckerman: Reflection and Action Under Scarce Resources: Theoretical Principles and Empirical Study. IJCAI 1989 : 1121-1127 David Heckerman: A Tractable Inference Algorithm for Diagnosing Multiple Diseases. UAI 1989 : 163-172 David Heckerman: An empirical comparison of three inference methods. UAI 1988 : 283-302 David Heckerman, Holly Brügge Jimison : A perspective on confidence and its use in focusing attention during knowledge acquisition. Int. J. Approx. Reasoning 2 (3): 336 (1988) David Heckerman, Eric Horvitz : On the Expressiveness of Rule-based Systems for Reasoning with Uncertainty. AAAI 1987 : 121-126 David Heckerman, Holly Brügge Jimison : A Bayesian Perspective on Confidence. UAI 1987 : 149-160 Ross D. Shachter , David Heckerman: Thinking Backward for Knowledge Acquisition. AI Magazine 8 (3): 55-61 (1987) Eric Horvitz , David Heckerman, Curtis Langlotz : A Framework for Comparing Alternative Formalisms for Plausible Reasoning. AAAI 1986 : 210-214 David Heckerman: An axiomatic framework for belief updates. UAI 1986 : 11-22 David Heckerman, Eric Horvitz : The myth of modularity in rule-based systems for reasoning with uncertainty. UAI 1986 : 23-34 Ross D. Shachter , David Heckerman: A backwards view for assessment. UAI 1986 : 317-324 Eric Horvitz , David Heckerman: The Inconsistent Use of Measures of Certainty in Artificial Intelligence Research. UAI 1985 : 137-152 David Heckerman: Probabilistic Interpretation for MYCIN's Certainty Factors. 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