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Bayesian network / Graphical model / Hierarchical Bayes model / Graph / Directed acyclic graph / Bayesian inference / Topology / Prior probability / Causality / Bayesian statistics / Statistics / Graph theory


Structured Priors for Structure Learning V. K. Mansinghka, C. Kemp, J. B. Tenenbaum
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Document Date: 2008-12-18 19:26:33

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Portland / Providence / Ordering / Scottland / Edinburgh / /

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Learning Module Networks / NTT Communication Sciences Laboratory / Cambridge University Press / Learning Parsimonious Regulatory Networks / Diagnostic Bayesian Networks / Learning Bayesian Networks / /

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Jordan / United Kingdom / /

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Linguistic Sciences Massachusetts Institute of Technology Brown University Cambridge / /

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real-world applications / inference algorithms / module networks / gene regulatory network / cognitive applications / genetic regulatory network / gene regulatory networks / learned networks / learning gene networks / search techniques / social networks / causal systems / real-world systems / probabilistic networks / learning algorithm / gene networks / /

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Cambridge University / Institute of Technology Brown University Cambridge / Saint Flour Summer School / UC Berkeley / Massachusetts Institute of Technology / UC Berkeley Statistics Department / /

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Graphical meta-model for the ordered blockmodel / advocate / representative / biologist / /

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Rhode Island / Massachusetts / /

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Machine Learning / Journal of Machine Learning Research / /

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expert system / learning algorithm / inference algorithms / artificial intelligence / Variational EM Algorithm / Data Mining / Machine Learning / MCMC algorithm / intelligent agent / /

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