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    UAI
    1996
    109views Artificial Intelligence» more  UAI 1996»
    A Graph-Theoretic Analysis of Information Value
    14 years 1 months ago
    A Graph-Theoretic Analysis of Information Value
    Download  research.microsoft.com
    We derive qualitative relationships about the informationalrelevance of variables in graphical decision models based on a consideration of the topology of the models. Speci cally,...
    Kim-Leng Poh, Eric Horvitz
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    UAI
    1996
    111views Artificial Intelligence» more  UAI 1996»
    Network Engineering for Complex Belief Networks
    14 years 1 months ago
    Network Engineering for Complex Belief Networks
    Download  www.mathcs.emory.edu
    Developing a large belief network, like any large system, requires systems engineering to manage the design and construction process. We propose that network engineering follow a ...
    Suzanne M. Mahoney, Kathryn B. Laskey
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    UAI
    1996
    168views Artificial Intelligence» more  UAI 1996»
    Bayesian Learning of Loglinear Models for Neural Connectivity
    14 years 1 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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    UAI
    1996
    126views Artificial Intelligence» more  UAI 1996»
    Computing upper and lower bounds on likelihoods in intractable networks
    14 years 1 months ago
    Computing upper and lower bounds on likelihoods in intractable networks
    Download  www.bitsavers.org
    Tommi Jaakkola, Michael I. Jordan
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    UAI
    1996
    107views Artificial Intelligence» more  UAI 1996»
    Efficient Search-Based Inference for noisy-OR Belief Networks: TopEpsilon
    14 years 1 months ago
    Efficient Search-Based Inference for noisy-OR Belief Networks: TopEpsilon
    Download  reference.kfupm.edu.sa
    Inference algorithms for arbitrary belief networks are impractical for large, complex belief networks. Inference algorithms for specialized classes of belief networks have been sh...
    Kurt Huang, Max Henrion
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    UAI
    1996
    102views Artificial Intelligence» more  UAI 1996»
    Why is diagnosis using belief networks insensitive to imprecision in probabilities?
    14 years 1 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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    UAI
    1996
    135views Artificial Intelligence» more  UAI 1996»
    Asymptotic Model Selection for Directed Networks with Hidden Variables
    14 years 1 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
    14 years 1 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
    14 years 1 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
    14 years 1 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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