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IJAR
2006
89views more  IJAR 2006»
13 years 8 months ago
Learning probabilistic decision graphs
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
ICIP
2003
IEEE
14 years 10 months ago
A variational method for Bayesian blind image deconvolution
In this paper the blind image deconvolution (BID) problem is solved using the Bayesian framework. In order to find the parameters of the proposed Bayesian model we present a new g...
Aristidis Likas, Nikolas P. Galatsanos
JCST
2010
139views more  JCST 2010»
13 years 7 months ago
Dirichlet Process Gaussian Mixture Models: Choice of the Base Distribution
In the Bayesian mixture modeling framework it is possible to infer the necessary number of components to model the data and therefore it is unnecessary to explicitly restrict the n...
Dilan Görür, Carl Edward Rasmussen
ICASSP
2009
IEEE
13 years 6 months ago
Spoken language interpretation: On the use of dynamic Bayesian networks for semantic composition
In the context of spoken language interpretation, this paper introduces a stochastic approach to infer and compose semantic structures. Semantic frame structures are directly deri...
Marie-Jean Meurs, Fabrice Lefevre, Renato de Mori
EKAW
2006
Springer
14 years 14 days ago
Iterative Bayesian Network Implementation by Using Annotated Association Rules
Abstract. This paper concerns the iterative implementation of a knowledge model in a data mining context. Our approach relies on coupling a Bayesian network design with an associat...
Clément Fauré, Sylvie Delprat, Jean-...