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    UAI
    2004
    207views Artificial Intelligence» more  UAI 2004»
    Bayesian Learning in Undirected Graphical Models: Approximate MCMC Algorithms
    13 years 8 months ago
    Bayesian Learning in Undirected Graphical Models: Approximate MCMC Algorithms
    Download  learning.eng.cam.ac.uk
    Bayesian learning in undirected graphical models--computing posterior distributions over parameters and predictive quantities-is exceptionally difficult. We conjecture that for ge...
    Iain Murray, Zoubin Ghahramani
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    IDA
    2005
    Springer
    151views Information Technology» more  IDA 2005»
    Bayesian Networks Learning for Gene Expression Datasets
    14 years 6 days ago
    Bayesian Networks Learning for Gene Expression Datasets
    Download  www.ing.unife.it
    DNA arrays yield a global view of gene expression and can be used to build genetic networks models, in order to study relations between genes. Literature proposes Bayesian network ...
    Giacomo Gamberoni, Evelina Lamma, Fabrizio Riguzzi...
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    BMCBI
    2010
    174views more  BMCBI 2010»
    The effect of prior assumptions over the weights in BayesPI with application to study protein-DNA interactions from ChIP-based h
    13 years 6 months ago
    The effect of prior assumptions over the weights in BayesPI with application to study protein-DNA interactions from ChIP-based h
    Download  www.biomedcentral.com
    Background: To further understand the implementation of hyperparameters re-estimation technique in Bayesian hierarchical model, we added two more prior assumptions over the weight...
    Junbai Wang
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    FLAIRS
    2006
    144views Artificial Intelligence» more  FLAIRS 2006»
    Decomposing Local Probability Distributions in Bayesian Networks for Improved Inference and Parameter Learning
    13 years 8 months ago
    Decomposing Local Probability Distributions in Bayesian Networks for Improved Inference and Parameter Learning
    Download  www.aaai.org
    A major difficulty in building Bayesian network models is the size of conditional probability tables, which grow exponentially in the number of parents. One way of dealing with th...
    Adam Zagorecki, Mark Voortman, Marek J. Druzdzel
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    BMCBI
    2011
    252views Artificial Intelligence» more  BMCBI 2011»
    A hierarchical Bayesian network approach for linkage disequilibrium modeling and data-dimensionality reduction prior to genome-w
    12 years 10 months ago
    A hierarchical Bayesian network approach for linkage disequilibrium modeling and data-dimensionality reduction prior to genome-w
    Download  www.biomedcentral.com
    Background: Discovering the genetic basis of common genetic diseases in the human genome represents a public health issue. However, the dimensionality of the genetic data (up to 1...
    Raphael Mourad, Christine Sinoquet, Philippe Leray
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