# Marginal model

## Papers overview

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Highly Cited

2013

Highly Cited

2013

- IEEE Transactions on Pattern Analysis and Machineâ€¦
- 2013

Likelihood-based learning of graphical models faces challenges of computational complexity and robustness to modelâ€¦Â (More)

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Highly Cited

2009

Highly Cited

2009

- 2009

We introduce a powerful and flexible MCMC algorithm for stochas-tic simulation. The method builds on a pseudo-marginal methodâ€¦Â (More)

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Highly Cited

2008

Highly Cited

2008

- American journal of epidemiology
- 2008

The method of inverse probability weighting (henceforth, weighting) can be used to adjust for measured confounding and selectionâ€¦Â (More)

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Highly Cited

2007

Highly Cited

2007

- 2007

Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at . http://www.jstorâ€¦Â (More)

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Highly Cited

2004

Highly Cited

2004

- 2004

We introduce a set of new Markov chain Monte Carlo algorithms for Bayesian analysis of the multinomial probit model. Our Bayesianâ€¦Â (More)

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Highly Cited

2003

Highly Cited

2003

- AISTATS
- 2003

The â€˜sparse Bayesianâ€™ modelling approach, as exemplified by the â€˜relevance vector machineâ€™, enables sparse classification andâ€¦Â (More)

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Highly Cited

2001

Highly Cited

2001

- 2001

This article provides a framework for estimating the marginal likelihood for the purpose of Bayesian model comparisons . Theâ€¦Â (More)

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Highly Cited

2000

Highly Cited

2000

- 2000

Robins (1993, 1994, 1997, 1998ab) has developed a set of causal or counterfactual models, the structural nested models (SNMsâ€¦Â (More)

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Highly Cited

1999

Highly Cited

1999

- Synthese
- 1999

The subject-specific data from a longitudinal study consist of a string of numbers. These numbers represent a series of empiricalâ€¦Â (More)

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Highly Cited

1995

Highly Cited

1995

- 1995

Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entireâ€¦Â (More)

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