- Advances in Probabilistic Reasoning Northrop Research and Technology Center
- Update on the Pathfinder Project David Heckerman, Eric Horvitz, and Bharat Nathwani
- Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
- A Bayesian Approach to Learning Bayesian Networks with Local David Maxwell Chickering
- Decision-Theoretic Case-Based Reasoning John S. Breese
- Fast Learning From Sparse Data David Maxwell Chickering and David Heckerman Microsoft Research
- On the Expressiveness of Rule-based Systems for Reasoning with Uncertainty
- Autoregressive Tree Models for Time-Series
- Toward Normative Expert Systems: Part II Probability-Based Representations for Efficient
- A Decision-Based View of Causality David Heckerman
- CFW: A Collaborative Filtering System Using Posteriors Over Weights of Evidence
- Heuristic Abstraction in the Decision-Theoretic Pathfinder System
- Probabilistic Similarity Networks By David E. Heckerman
- Learning Mixtures of DAG Models Bo Thiesson, Christopher Meek, David Maxwell Chickering, and David Heckerman
- A Decision-Based View of Causality David Heckerman
- Toward Normative Expert Systems: Part I The Pathfinder Project
- Troubleshooting under Uncertainty David Heckerman John S. Breese
- Learning mixtures of smooth, nonuniform deformation models for probabilistic
- A Framework for Comparing Alternative Formalisms for Plausible Reasoning Eric J. Horvitz, David E. Heckerman, Curtis P. Langlotz
- A Bayesian Perspective on Confidence David Heckerman and Holly Jimison
- An Approximate Nonmyopic Computation for Value of Information David Heckerman
- Asymptotic Model Selection for Directed Networks with Hidden Computer Science Department
- An Approximate Nonmyopic Computation for Value of Information
- Diagnosis of Multiple Faults: A Sensitivity Analysis David Heckerman
- Probabilistic Models for Relational Data David Heckerman, Christopher Meek, and Daphne Koller
- From Certainty Factors to Belief Networks David E. Heckerman
- Staged Mixture Modeling and Boosting Christopher Meek, Bo Thiesson and David Heckerman
- Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network
- Inferring Informational Goals from Free-Text Queries: A Bayesian Approach
- A Tractable Inference Algorithm for Diagnosing Multiple Diseases1
- Problem Formulation as the Reduction of a Decision Model David E. Heckerman Eric J. Horvitz
- A De nition and Graphical Representation for Causality David Heckerman
- Causal Independence for Knowledge Acquisition and Inference David Heckerman
- The Compilation of Decision Models David E. Heckerman
- Inference Algorithms for Similarity Networks Department of Computer Science
- Decision-Theoretic Troubleshooting: A Framework for Repair and Experiment
- Decision-Theoretic Troubleshooting David Heckerman John S. Breese Koos Rommelse
- A Characterization of the Bivariate Normal-Wishart Distribution
- Similarity Networks for the Construction of Multiple-Fault Belief Networks
- A Bayesian Approach to Learning Causal Networks David Heckerman
- Probabilistic Similarity Networks GREAT THINKERS
- Determining the Number of Non-Spurious Arcs in a Learned DAG Model: Investigation of a Bayesian and a Frequentist Approach
- An MDP-Based Recommender System Department of Computer Science
- Variations on Undirected Graphical Models and their Relationships David Heckerman
- Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributions
- A Bayesian Approach to Filtering Junk E-Mail Mehran Sahami Susan Dumaisy David Heckermany Eric Horvitzy
- An Experimental Comparison of Several Clustering and Initialization Methods
- Computationally Efficient Methods for Selecting Among Mixtures of Graphical Models
- Structure and Parameter Learning for Causal Independence and Causal Interaction Models
- Decision-Theoretic Case-Based Reasoning John S. Breese and David Heckerman
- Causal Independence for Probability Assessment and Inference Using Bayesian Networks
- A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks
- Learning Bayesian Networks: A Uni cation for Discrete and Gaussian Domains
- The Certainty-Factor Model David Heckerman
- Separable and Transitive Graphoids Northrop Research and
- Re ection and Action Under Scarce Resources: Theoretical Principles and Empirical Study
- An Empirical Comparison of Three Inference Methods David Heckerman
- An Evaluation of the Diagnostic Accuracy of David E. Heckerman
- Probabilistic Independence Networks for Hidden Markov Probability Models
- A New Look at Causal Independence David Heckerman John S. Breese
- Thinking Backward for Knowledge Acquisition
- Pearl Causality and the Value of Control Ross Shachter and David Heckerman