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Most tasks require a person or an automated system to reason -- to reach conclusions based on available information. The… Expand The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random… Expand We propose penalized likelihood methods for estimating the concentration matrix in the Gaussian graphical model. The methods lead… Expand This paper presents a tutorial introduction to the use of variational methods for inference and learning in graphical models… Expand JAGS is a program for Bayesian Graphical modelling which aims for compatibility with Classic BUGS. The program could eventually… Expand This paper presents a novel practical framework for Bayesian model averaging and model selection in probabilistic graphical… Expand Part 1 Inference: introduction to inference for Bayesian networks, Robert Cowell advanced inference in Bayesian networks, Robert… Expand Graphs and Conditional Independence.- Log-Linear Models.- Bayesian Networks.- Gaussian Graphical Models.- Mixed Interaction… Expand Pendant plus d'un demi-siecle, les graphes ont ete utilises pour representer des modeles statistiques en analyse de donnees. En… Expand The Wiley Paperback Series makes valuable content more accessible to a new generation of statisticians, mathematicians and… Expand