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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… 
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Papers overview

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Highly Cited
2009
Highly Cited
2009
Most tasks require a person or an automated system to reason -- to reach conclusions based on available information. The… 
Highly Cited
2008
Highly Cited
2008
The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random… 
Highly Cited
2007
Highly Cited
2007
We propose penalized likelihood methods for estimating the concentration matrix in the Gaussian graphical model. The methods lead… 
Review
2004
Review
2004
This paper presents a tutorial introduction to the use of variational methods for inference and learning in graphical models… 
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… 
Highly Cited
1999
Highly Cited
1999
This paper presents a novel practical framework for Bayesian model averaging and model selection in probabilistic graphical… 
Review
1998
Review
1998
Part 1 Inference: introduction to inference for Bayesian networks, Robert Cowell advanced inference in Bayesian networks, Robert… 
Highly Cited
1996
Highly Cited
1996
Graphs and Conditional Independence.- Log-Linear Models.- Bayesian Networks.- Gaussian Graphical Models.- Mixed Interaction… 
Highly Cited
1995
Highly Cited
1995
Pendant plus d'un demi-siecle, les graphes ont ete utilises pour representer des modeles statistiques en analyse de donnees. En… 
Review
1994
Review
1994
  • Wray L. Buntine
  • Journal of Artificial Intelligence Research
  • 1994
  • Corpus ID: 11672931
This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known…