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» Complexity of Inference in Graphical Models
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ICPR
2002
IEEE
14 years 8 months ago
Boosting and Structure Learning in Dynamic Bayesian Networks for Audio-Visual Speaker Detection
Bayesian networks are an attractive modeling tool for human sensing, as they combine an intuitive graphical representation with ef?cient algorithms for inference and learning. Ear...
Tanzeem Choudhury, James M. Rehg, Vladimir Pavlovi...
ATAL
2010
Springer
13 years 8 months ago
History-dependent graphical multiagent models
A dynamic model of a multiagent system defines a probability distribution over possible system behaviors over time. Alternative representations for such models present tradeoffs i...
Quang Duong, Michael P. Wellman, Satinder P. Singh...
TSP
2008
179views more  TSP 2008»
13 years 7 months ago
Estimation in Gaussian Graphical Models Using Tractable Subgraphs: A Walk-Sum Analysis
Graphical models provide a powerful formalism for statistical signal processing. Due to their sophisticated modeling capabilities, they have found applications in a variety of fie...
V. Chandrasekaran, Jason K. Johnson, Alan S. Wills...
CIBCB
2006
IEEE
14 years 1 months ago
A Novel Graphical Model Approach to Segmenting Cell Images
— Successful biological image analysis usually requires satisfactory segmentations to identify regions of interest as an intermediate step. Here we present a novel graphical mode...
Shann-Ching Chen, Ting Zhao, Geoffrey J. Gordon, R...
BMCBI
2007
129views more  BMCBI 2007»
13 years 7 months ago
Inferring cellular networks - a review
In this review we give an overview of computational and statistical methods to reconstruct cellular networks. Although this area of research is vast and fast developing, we show t...
Florian Markowetz, Rainer Spang