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Junction tree algorithm

Known as: JTA, Junction-tree algorithm 
The junction tree algorithm (also known as 'Clique Tree') is a method used in machine learning to extract marginalization in general graphs. In… Expand
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Papers overview

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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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