Active Learning for Structure in Bayesian Networks

Active Learning for Structure in Bayesian Networks (2001)

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by Simon Tong , Daphne Koller

Venue:	IN INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE
Citations:	38 - 2 self

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@INPROCEEDINGS{Tong01activelearning,
    author = {Simon Tong and Daphne Koller},
    title = {Active Learning for Structure in Bayesian Networks},
    booktitle = {IN INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE},
    year = {2001},
    pages = {863--869},
    publisher = {}
}

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Abstract

The task of causal structure discovery from empirical data is a fundamental problem in many areas. Experimental data is crucial for accomplishing this task. However, experiments are typically expensive, and must be selected with great care. This paper

Citations

5664	Probabilistic Reasoning in Intelligent Systems - Pearl - 1988
1081	Local Computations with Probabilities on graphical structures and their applications to expert systems (with discussion - Lauritzen, Spiegelhalter
766	Causality: Models, reasoning, and inference - Pearl - 2001
752	Learning Bayesian networks: the combination of knowledge and statistical data - Heckerman, Geiger, et al. - 1995
384	Causation, prediction and search - Spirtes, Glymour, et al. - 1993
156	Being Bayesian about network structure. a Bayesian approach to structure discovery in Bayesian networks - Friedman, Koller
153	Theory refinement on bayesian networks - Buntine - 1991
141	Learning Bayesian network structure from massive datasets: The ”sparse candidate” algorithm - Friedman, Nachman, et al. - 1999
64	C: A Bayesian approach to causal discovery - Cooper, Heckerman, et al.
51	Causal discovery from a mixture of experimental and observational data - Cooper, Yoo - 1999
40	Active learning for parameter estimation in bayesian networks - Tong, Koller - 2000
4	One hundred years of the design of experiments on and off the pages of "Biometrika". Biometrika - Atkinson, Bailey - 2001