A Tutorial on Learning With Bayesian Networks

Feb 01, 2020
David Heckerman

* Original version published in Learning in Graphical Models, M. Jordan, ed., MIT Press, Cambridge, MA, 1999 
* Addresses errors in the section on learning causal relationships 

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Probabilistic Similarity Networks

Nov 06, 2019
David Heckerman

* Probabilistic Similarity Networks. MIT Press, Cambridge, MA, 1991 

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Embedded Bayesian Network Classifiers

Oct 22, 2019
David Heckerman, Chris Meek


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Accounting for hidden common causes when inferring cause and effect from observational data

Jan 03, 2018
David Heckerman

* Presented at the NIPS workshop on causal inference (NIPS 2017), Long Beach, CA, USA 

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Dependence and Relevance: A probabilistic view

Oct 27, 2016
Dan Geiger, David Heckerman


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Decision-Theoretic Troubleshooting: A Framework for Repair and Experiment

May 17, 2015
John S. Breese, David Heckerman

* Appears in Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI1996) 

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Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network

May 17, 2015
David Maxwell Chickering, David Heckerman

* Appears in Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI1996) 

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A New Look at Causal Independence

May 17, 2015
David Heckerman, John S. Breese

* Appears in Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (UAI1994) 

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An Empirical Comparison of Three Inference Methods

May 17, 2015
David Heckerman

* Appears in Proceedings of the Fourth Conference on Uncertainty in Artificial Intelligence (UAI1988) 

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Separable and transitive graphoids

May 16, 2015
Dan Geiger, David Heckerman

* Appears in Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence (UAI1990) 

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Similarity Networks for the Construction of Multiple-Faults Belief Networks

May 16, 2015
David Heckerman

* Appears in Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence (UAI1990) 

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Advances in Probabilistic Reasoning

May 16, 2015
Dan Geiger, David Heckerman

* Appears in Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence (UAI1991) 

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An Approximate Nonmyopic Computation for Value of Information

May 16, 2015
David Heckerman, Eric J. Horvitz, Blackford Middleton

* Appears in Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence (UAI1991) 

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Problem Formulation as the Reduction of a Decision Model

May 16, 2015
David Heckerman, Eric J. Horvitz

* Appears in Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence (UAI1990) 

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Diagnosis of Multiple Faults: A Sensitivity Analysis

May 16, 2015
David Heckerman, Michael Shwe

* Appears in Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence (UAI1993) 

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Causal Independence for Knowledge Acquisition and Inference

May 16, 2015
David Heckerman

* Appears in Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence (UAI1993) 

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Inference Algorithms for Similarity Networks

May 16, 2015
Dan Geiger, David Heckerman

* Appears in Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence (UAI1993) 

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A Decision-Based View of Causality

May 16, 2015
David Heckerman, Ross D. Shachter

* Appears in Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (UAI1994) 

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Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

May 16, 2015
David Heckerman, Dan Geiger, David Maxwell Chickering

* Appears in Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (UAI1994) 

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A Definition and Graphical Representation for Causality

May 16, 2015
David Heckerman, Ross D. Shachter

* Appears in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI1995) 

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A Bayesian Approach to Learning Causal Networks

May 16, 2015
David Heckerman

* Appears in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI1995) 

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Asymptotic Model Selection for Directed Networks with Hidden Variables

May 16, 2015
Dan Geiger, David Heckerman, Christopher Meek

* Appears in Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI1996) 

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Structure and Parameter Learning for Causal Independence and Causal Interaction Models

May 16, 2015
Christopher Meek, David Heckerman

* Appears in Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI1997) 

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A Bayesian Approach to Learning Bayesian Networks with Local Structure

May 16, 2015
David Maxwell Chickering, David Heckerman, Christopher Meek

* Appears in Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI1997) 

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Learning Mixtures of DAG Models

May 16, 2015
Bo Thiesson, Christopher Meek, David Maxwell Chickering, David Heckerman

* Appears in Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI1998) 

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An Experimental Comparison of Several Clustering and Initialization Methods

May 16, 2015
Marina Meila, David Heckerman

* Appears in Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI1998) 

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Inferring Informational Goals from Free-Text Queries: A Bayesian Approach

May 16, 2015
David Heckerman, Eric J. Horvitz

* Appears in Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI1998) 

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Fast Learning from Sparse Data

May 16, 2015
David Maxwell Chickering, David Heckerman

* Appears in Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI1999) 

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Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributions

May 16, 2015
Dan Geiger, David Heckerman

* Appears in Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI1999) 

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CFW: A Collaborative Filtering System Using Posteriors Over Weights Of Evidence

May 16, 2015
Carl Kadie, Christopher Meek, David Heckerman

* Appears in Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (UAI2002) 

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