Graphical model

Known as: Graphical Models, Probabilistic graphical model 
A graphical model or probabilistic graphical model (PGM) is a probabilistic model for which a graph expresses the conditional dependence structure… (More)
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Papers overview

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Highly Cited
2007
Highly Cited
2007
We propose penalized likelihood methods for estimating the concentration matrix in the Gaussian graphical model. The methods lead… (More)
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Highly Cited
2007
Highly Cited
2007
The supremacy of n-gram models in statistical language modelling has recently been challenged by parametric models that use… (More)
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Highly Cited
2003
Highly Cited
2003
JAGS is a program for Bayesian Graphical modelling which aims for compatibility with Classic BUGS. The program could eventually… (More)
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Highly Cited
2002
Highly Cited
2002
We present an efficient procedure for estimating the marginal likelihood of probabilistic models with latent variables or… (More)
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Highly Cited
2001
Highly Cited
2001
We introduce a compact graph-theoretic representation for multi-party game theory. Our main result is a provably correct and… (More)
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Highly Cited
2000
Highly Cited
2000
Graphical models, such as Bayesian networks and Markov networks, represent joint distributions over a set of variables by means… (More)
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Highly Cited
1999
Highly Cited
1999
Graphical models, such as Bayesian networks and Markov random fields, represent statistical dependencies of variables by a graph… (More)
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Highly Cited
1999
Highly Cited
1999
This paper presents a novel practical framework for Bayesian model averaging and model selection in probabilistic graphical… (More)
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Highly Cited
1993
Highly Cited
1993
We consider the problem of model selection and accounting for model uncertainty in high dimensional contingency tables motivated… (More)
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Highly Cited
1993
Highly Cited
1993
Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at . http://www.jstor… (More)
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