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    UAI
    1996
    135views Artificial Intelligence» more  UAI 1996»
    Asymptotic Model Selection for Directed Networks with Hidden Variables
    13 years 6 months ago
    Asymptotic Model Selection for Directed Networks with Hidden Variables
    Download  research.microsoft.com
    We extend the Bayesian Information Criterion (BIC), an asymptotic approximation for the marginal likelihood, to Bayesian networks with hidden variables. This approximation can be ...
    Dan Geiger, David Heckerman, Christopher Meek
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    UAI
    1996
    105views Artificial Intelligence» more  UAI 1996»
    A Qualitative Markov Assumption and Its Implications for Belief Change
    13 years 6 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
    108views Artificial Intelligence» more  UAI 1996»
    Learning Bayesian Networks with Local Structure
    13 years 6 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
    147views Artificial Intelligence» more  UAI 1996»
    An evaluation of structural parameters for probabilistic reasoning: Results on benchmark circuits
    13 years 6 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
    84views Artificial Intelligence» more  UAI 1996»
    Sound Abstraction of Probabilistic Actions in The Constraint Mass Assignment Framework
    13 years 6 months ago
    Sound Abstraction of Probabilistic Actions in The Constraint Mass Assignment Framework
    Download  www.iist.unu.edu
    AnHai Doan, Peter Haddawy
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    UAI
    1996
    94views Artificial Intelligence» more  UAI 1996»
    Topological parameters for time-space tradeoff
    13 years 6 months ago
    Topological parameters for time-space tradeoff
    Download  www.ics.uci.edu
    In this paper we propose a family of algorithms combining treeclustering with conditioning that trade space for time. Such algorithms are useful for reasoning in probabilistic and...
    Rina Dechter
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    UAI
    1996
    120views Artificial Intelligence» more  UAI 1996»
    Bucket elimination: A unifying framework for probabilistic inference
    13 years 6 months ago
    Bucket elimination: A unifying framework for probabilistic inference
    Download  www.ics.uci.edu
    Probabilistic inference algorithms for belief updating, nding the most probable explanation, the maximum a posteriori hypothesis, and the maximum expected utility are reformulated...
    Rina Dechter
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    UAI
    1996
    84views Artificial Intelligence» more  UAI 1996»
    Query DAGs: A practical paradigm for implementing belief-network inference
    13 years 6 months ago
    Query DAGs: A practical paradigm for implementing belief-network inference
    Download  www.jair.org
    Adnan Darwiche, Gregory M. Provan
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    UAI
    1996
    126views Artificial Intelligence» more  UAI 1996»
    Some Experiments with Real-time Decision Algorithms
    13 years 6 months ago
    Some Experiments with Real-time Decision Algorithms
    Download  web.engr.oregonstate.edu
    Real-time Decision algorithms are a class of incremental resource-bounded [Horvitz, 89] or anytime [Dean, 93] algorithms for evaluating influence diagrams. We present a test domai...
    Bruce D'Ambrosio, Scott Burgess
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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 6 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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