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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 7 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
    151views Artificial Intelligence» more  UAI 1996»
    Learning Equivalence Classes of Bayesian Network Structures
    13 years 7 months ago
    Learning Equivalence Classes of Bayesian Network Structures
    Download  jmlr.csail.mit.edu
    Two Bayesian-network structures are said to be equivalent if the set of distributions that can be represented with one of those structures is identical to the set of distributions...
    David Maxwell Chickering
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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 7 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
    105views Artificial Intelligence» more  UAI 1996»
    A Qualitative Markov Assumption and Its Implications for Belief Change
    13 years 7 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
    168views Artificial Intelligence» more  UAI 1996»
    Bayesian Learning of Loglinear Models for Neural Connectivity
    13 years 7 months ago
    Bayesian Learning of Loglinear Models for Neural Connectivity
    Download  www.mpib-berlin.mpg.de
    This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies. A major objective of the research is t...
    Kathryn B. Laskey, Laura Martignon
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