22 search results - page 2 / 5 » Bayesian Learning in Undirected Graphical Models: Approximat... |
108
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ICML
2004
IEEE
16 years 12 days ago
2004
IEEE
Variational inference methods, including mean field methods and loopy belief propagation, have been widely used for approximate probabilistic inference in graphical models. While ...
105
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ICML
2010
IEEE
15 years 20 days ago
2010
IEEE
Learning undirected graphical models such as Markov random fields is an important machine learning task with applications in many domains. Since it is usually intractable to learn...
115
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JMLR
2010
14 years 6 months ago
2010
Learning dynamic Bayesian network structures provides a principled mechanism for identifying conditional dependencies in time-series data. An important assumption of traditional D...
JETAI
1998
14 years 11 months ago
1998
Graphical structures such as Bayesian networks or Markov networks are very useful tools for representing irrelevance or independency relationships, and they may be used to e cientl...
COLT
2004
Springer
15 years 5 months ago
2004
Springer
: We introduce a graph-theoretic generalization of classical Arrow-Debreu economics, in which an undirected graph specifies which consumers or economies are permitted to engage in...