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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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Related topics

Related topics

10 relations
Bayesian networkBelief propagationCycle (graph theory)Decomposition method (constraint satisfaction)
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Papers overview

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2018
2018
Lifted Dynamic Junction Tree Algorithm
Probabilistic models involving relational and temporal aspects need exact and efficient inference algorithms. Existing approaches… Expand
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2016
2016
Lifted Junction Tree Algorithm
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
The junction tree algorithm
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
Exploiting Within-Clique Factorizations in Junction-Tree Algorithms
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
A DBN inference algorithm using junction tree
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
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
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
Propagation Algorithms for Variational Bayesian Learning
Variational approximations are becoming a widespread tool for Bayesian learning of graphical models. We provide some theoretical… Expand
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2000
2000
Microwave device modeling exploiting generalized space mapping
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
Variational Approximations between Mean Field Theory and the Junction Tree Algorithm
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
A Variational Baysian Framework for Graphical Models
This paper presents a novel practical framework for Bayesian model averaging and model selection in probabilistic graphical… Expand
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