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    UAI
    1996
    84views Artificial Intelligence» more  UAI 1996»
    Sound Abstraction of Probabilistic Actions in The Constraint Mass Assignment Framework
    14 years 1 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
    14 years 1 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
    14 years 1 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
    14 years 1 months ago
    Query DAGs: A practical paradigm for implementing belief-network inference
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    Adnan Darwiche, Gregory M. Provan
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    UAI
    1996
    126views Artificial Intelligence» more  UAI 1996»
    Some Experiments with Real-time Decision Algorithms
    14 years 1 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
    14 years 1 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
    151views Artificial Intelligence» more  UAI 1996»
    Learning Equivalence Classes of Bayesian Network Structures
    14 years 1 months ago
    Learning Equivalence Classes of Bayesian Network Structures
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    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
    146views Artificial Intelligence» more  UAI 1996»
    Context-Specific Independence in Bayesian Networks
    14 years 1 months ago
    Context-Specific Independence in Bayesian Networks
    Download  robotics.stanford.edu
    Bayesiannetworks provide a languagefor qualitatively representing the conditional independence properties of a distribution. This allows a natural and compact representation of th...
    Craig Boutilier, Nir Friedman, Moisés Golds...
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    UAI
    1996
    101views Artificial Intelligence» more  UAI 1996»
    Decision-Theoretic Troubleshooting: A Framework for Repair and Experiment
    14 years 1 months ago
    Decision-Theoretic Troubleshooting: A Framework for Repair and Experiment
    Download  reference.kfupm.edu.sa
    John S. Breese, David Heckerman
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    UAI
    1994
    116views Artificial Intelligence» more  UAI 1994»
    Belief Induced by the Partial Knowledge of the Probabilities
    14 years 1 months ago
    Belief Induced by the Partial Knowledge of the Probabilities
    Download  iridia0.ulb.ac.be
    : We construct the belief function that quantifies the agent' beliefs about which event of will occurred when he knows that the event is selected by a chance set-up and that ...
    Philippe Smets
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