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    UAI
    1996
    120views Artificial Intelligence» more  UAI 1996»
    Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network
    13 years 11 months ago
    Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network
    Download  research.microsoft.com
    We discuss Bayesian methods for learning Bayesian networks when data sets are incomplete. In particular, we examine asymptotic approximations for the marginal likelihood of incomp...
    David Maxwell Chickering, David Heckerman
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    UAI
    1996
    147views Artificial Intelligence» more  UAI 1996»
    An evaluation of structural parameters for probabilistic reasoning: Results on benchmark circuits
    13 years 11 months ago
    An evaluation of structural parameters for probabilistic reasoning: Results on benchmark circuits
    Download  www.ics.uci.edu
    Many algorithms for processing probabilistic networks are dependent on the topological properties of the problem's structure. Such algorithmse.g., clustering, conditioning ar...
    Yousri El Fattah, Rina Dechter
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    UAI
    1996
    108views Artificial Intelligence» more  UAI 1996»
    Learning Bayesian Networks with Local Structure
    13 years 11 months ago
    Learning Bayesian Networks with Local Structure
    Download  robotics.stanford.edu
    In this paper we examine a novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks. Our approach explicitly ...
    Nir Friedman, Moisés Goldszmidt
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    UAI
    1996
    105views Artificial Intelligence» more  UAI 1996»
    A Qualitative Markov Assumption and Its Implications for Belief Change
    13 years 11 months ago
    A Qualitative Markov Assumption and Its Implications for Belief Change
    Download  www.cs.cornell.edu
    The study of belief change has been an active area in philosophy and AI. In recent years, two special cases of belief change, belief revision and belief update, have been studied ...
    Nir Friedman, Joseph Y. Halpern
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    UAI
    1996
    102views Artificial Intelligence» more  UAI 1996»
    Why is diagnosis using belief networks insensitive to imprecision in probabilities?
    13 years 11 months ago
    Why is diagnosis using belief networks insensitive to imprecision in probabilities?
    Download  bmir.stanford.edu
    Recent research has found that diagnostic performance with Bayesian belief networks is often surprisingly insensitive to imprecision in the numerical probabilities. For example, t...
    Max Henrion, Malcolm Pradhan, Brendan Del Favero, ...
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