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Likelihoods and Parameter Priors for Bayesian Networks



David Heckerman , Dan Geiger

* This manuscript, originally appearing as a Nov 1995 Microsoft Research tech report, is a precursor to the Annals Oct 2002 publication (arXiv:2105.03248) with material on distribution equivalence that was omitted from the 2002 publication. This version contains corrections and updates to the 1995 tech report 

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



Dan Geiger , David Heckerman

* The Annals of Statistics, 30: 1412-1440, 2002 
* Annals October 2002 version with corrections and updates made May 2021 

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A Tutorial on Learning With Bayesian Networks



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



David Heckerman

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

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Embedded Bayesian Network Classifiers



David Heckerman , Chris Meek


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



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



Dan Geiger , David Heckerman


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Decision-Theoretic Troubleshooting: A Framework for Repair and Experiment



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



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



David Heckerman , John S. Breese

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

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