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2018

2018

Probabilistic models involving relational and temporal aspects need exact and efficient inference algorithms. Existing approaches… Expand

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2016

2016

We look at probabilistic first-order formalisms where the domain objects are known. In these formalisms, the standard approach… Expand

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2011

2011

In the previous scribes, we introduced general objects used in graphical models as well message passing schemes such as the sum… Expand

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2010

2010

We show that the expected computational complexity of the Junction-Tree Algorithm for maximum a posteriori inference in graphical… Expand

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2004

2004

Dynamic bayesian networks (DBNs) is a compact representation of complex stochastic processes and has been used for many purposes… Expand

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Highly Cited

2000

Highly Cited

2000

Particle filters (PFs) are powerful sampling-based inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow… Expand

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Highly Cited

2000

Highly Cited

2000

Variational approximations are becoming a widespread tool for Bayesian learning of graphical models. We provide some theoretical… Expand

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2000

2000

We present a comprehensive framework to engineering device modeling which we call Generalized Space Mapping (GSM). GSM… Expand

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Highly Cited

2000

Highly Cited

2000

Recently, variational approximations such as the mean field approximation have received much interest. We extend the standard… Expand

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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… Expand

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