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    AAAI
    2006
    101views Intelligent Agents» more  AAAI 2006»
    Performing Incremental Bayesian Inference by Dynamic Model Counting
    13 years 9 months ago
    Performing Incremental Bayesian Inference by Dynamic Model Counting
    Download  www.aaai.org
    The ability to update the structure of a Bayesian network when new data becomes available is crucial for building adaptive systems. Recent work by Sang, Beame, and Kautz (AAAI 200...
    Wei Li 0002, Peter van Beek, Pascal Poupart
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    BIOCOMP
    2006
    233views Bioinformatics» more  BIOCOMP 2006»
    Dynamic Bayesian Network (DBN) with Structure Expectation Maximization (SEM) for Modeling of Gene Network from Time Series Gene
    13 years 9 months ago
    Dynamic Bayesian Network (DBN) with Structure Expectation Maximization (SEM) for Modeling of Gene Network from Time Series Gene
    Download  ww1.ucmss.com
    Exploring gene regulatory network is a key topic in molecular biology. In this paper, we present a new dynamic Bayesian network (DBN) framework embedded with structural expectatio...
    Yu Zhang, Zhidong Deng, Hongshan Jiang, Peifa Jia
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    UAI
    1996
    135views Artificial Intelligence» more  UAI 1996»
    Asymptotic Model Selection for Directed Networks with Hidden Variables
    13 years 8 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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    IMDS
    2006
    33views more  IMDS 2006»
    Operations strategy and flexibility: modeling with Bayesian classifiers
    13 years 7 months ago
    Operations strategy and flexibility: modeling with Bayesian classifiers
    Download  hera.ugr.es
    Purpose
    María M. Abad-Grau, Daniel Arias-Aranda
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    IJAR
    2006
    89views more  IJAR 2006»
    Learning probabilistic decision graphs
    13 years 7 months ago
    Learning probabilistic decision graphs
    Download  www.cs.aau.dk
    Probabilistic decision graphs (PDGs) are a representation language for probability distributions based on binary decision diagrams. PDGs can encode (context-specific) independence...
    Manfred Jaeger, Jens D. Nielsen, Tomi Silander
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