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Propagation Standard Belief Propagation
, 2010
"... graph is a tree or can be expressed as a tree. Marginalization calculated as a propagation of local products and sumsStandard Belief Propagation Messages derived for SumProduct Algorithm are independent of Topology Can apply SumProduct to an arbitrary graph (Standard BP) ..."
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graph is a tree or can be expressed as a tree. Marginalization calculated as a propagation of local products and sumsStandard Belief Propagation Messages derived for SumProduct Algorithm are independent of Topology Can apply SumProduct to an arbitrary graph (Standard BP)
Belief Propagation and Statistical Physics
 Princeton University
, 2002
"... It was shown recently in [1] that there is a close connection between the belief propagation algorithm and certain approximations to the variational free energy in statistical physics. Specifically, the fixed points of the belief propagation algorithm are shown to coincide with the stationary points ..."
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Cited by 29 (2 self)
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It was shown recently in [1] that there is a close connection between the belief propagation algorithm and certain approximations to the variational free energy in statistical physics. Specifically, the fixed points of the belief propagation algorithm are shown to coincide with the stationary
Correctness of belief propagation in Gaussian graphical models of arbitrary topology
 NEURAL COMPUTATION
, 1999
"... Local "belief propagation" rules of the sort proposed byPearl [12] are guaranteed to converge to the correct posterior probabilities in singly connected graphical models. Recently, a number of researchers have empirically demonstrated good performance of "loopy belief propagation&q ..."
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Cited by 296 (7 self)
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Local "belief propagation" rules of the sort proposed byPearl [12] are guaranteed to converge to the correct posterior probabilities in singly connected graphical models. Recently, a number of researchers have empirically demonstrated good performance of "loopy belief propagation
Hybrid loopy belief propagation
, 2006
"... We propose an algorithm called Hybrid Loopy Belief Propagation (HLBP), which extends the Loopy Belief Propagation (LBP) (Murphy et al., 1999) and Nonparametric Belief Propagation (NBP) (Sudderth et al., 2003) algorithms to deal with general hybrid Bayesian networks. The main idea is to represent the ..."
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Cited by 8 (2 self)
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We propose an algorithm called Hybrid Loopy Belief Propagation (HLBP), which extends the Loopy Belief Propagation (LBP) (Murphy et al., 1999) and Nonparametric Belief Propagation (NBP) (Sudderth et al., 2003) algorithms to deal with general hybrid Bayesian networks. The main idea is to represent
Accuracy Bounds for Belief Propagation
, 2007
"... The belief propagation algorithm is widely applied to perform approximate inference on arbitrary graphical models, in part due to its excellent empirical properties and performance. However, little is known theoretically about when this algorithm will perform well. Using recent analysis of convergen ..."
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Cited by 19 (1 self)
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The belief propagation algorithm is widely applied to perform approximate inference on arbitrary graphical models, in part due to its excellent empirical properties and performance. However, little is known theoretically about when this algorithm will perform well. Using recent analysis
Fractional Belief Propagation
 in NIPS
, 2003
"... We consider approximate inference in probabilistic graphical models with approximate free energy methods. By considering equivalent factorgraph representations of a probabilistic model, we write down a family of different approximate treelike free energies. We show that this family interpolates be ..."
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Cited by 46 (1 self)
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between the naive meanfield free energy and the Bethe free energy. We derive fixedpoint equations that lead to fractional belief propagation algorithms, which include standard meanfield equations and loopy belief propagation as special cases. Using a cavityfield argument, we compute the fractional
Counting belief propagation
 In Proc. UAI09
, 2009
"... A major benefit of graphical models is that most knowledge is captured in the model structure. Many models, however, produce inference problems with a lot of symmetries not reflected in the graphical structure and hence not exploitable by efficient inference techniques such as belief propagation (BP ..."
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Cited by 53 (20 self)
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A major benefit of graphical models is that most knowledge is captured in the model structure. Many models, however, produce inference problems with a lot of symmetries not reflected in the graphical structure and hence not exploitable by efficient inference techniques such as belief propagation
Lifted firstorder belief propagation
 In Association for the Advancement of Artificial Intelligence (AAAI
, 2008
"... Unifying firstorder logic and probability is a longstanding goal of AI, and in recent years many representations combining aspects of the two have been proposed. However, inference in them is generally still at the level of propositional logic, creating all ground atoms and formulas and applying s ..."
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Cited by 115 (15 self)
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of the variable elimination algorithm, but it is extremely complex, generally does not scale to realistic domains, and has only been applied to very small artificial problems. In this paper we propose the first lifted version of a scalable probabilistic inference algorithm, belief propagation (loopy or not). Our
Parallel Splash Belief Propagation
"... As computer architectures transition towards exponentially increasing parallelism we are forced to adopt parallelism at a fundamental level in the design of machine learning algorithms. In this paper we focus on parallel graphical model inference. We demonstrate that the natural, synchronous paralle ..."
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Cited by 2 (0 self)
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parallelization of belief propagation is highly inefficient. By bounding the achievable parallel performance in chain graphical models we develop a theoretical understanding of the parallel limitations of belief propagation. We then provide a new parallel belief propagation algorithm which achieves optimal
Nonparanormal belief propagation (NPNBP
 In Advances in Neural Information Processing Systems (NIPS
"... The empirical success of the belief propagation approximate inference algorithm has inspired numerous theoretical and algorithmic advances. Yet, for continuous nonGaussian domains performing belief propagation remains a challenging task: recent innovations such as nonparametric or kernel belief pro ..."
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Cited by 1 (0 self)
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The empirical success of the belief propagation approximate inference algorithm has inspired numerous theoretical and algorithmic advances. Yet, for continuous nonGaussian domains performing belief propagation remains a challenging task: recent innovations such as nonparametric or kernel belief
Results 11  20
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2,196