# Graphical model

## Papers overview

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Highly Cited

2007

Highly Cited

2007

- 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

- ICML
- 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

- 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

- 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

- UAI
- 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

- Neural Computation
- 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

- Neural Computation
- 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

- NIPS
- 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

- 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

- 1993

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