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Loopy Substructural Local Search for the Bayesian Optimization Algorithm
[chapter]
2009
Lecture Notes in Computer Science
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This paper presents a local search method for the Bayesian optimization algorithm (BOA) based on the concepts of substructural neighborhoods and loopy belief propagation. ...
Abstract This paper presents a local search method for the Bayesian optimization algorithm (BOA) based on the concepts of substructural neighborhoods and loopy belief propagation. ...
When BP is applied to cyclic graphs it is often referred as loopy belief propagation (LBP). ...
doi:10.1007/978-3-642-03751-1_5
fatcat:vlxxqdosnjewpeigbwdv5hkquq
An Importance Sampling Algorithm Based on Evidence Pre-propagation
[article]
2012
arXiv
pre-print
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methods: loopy belief Propagation and e-cutoff. ...
To address this problem, we propose the Evidence Pre-propagation Importance Sampling algorithm (EPIS-BN), an importance sampling algorithm that computes an approximate importance function by the heuristic ...
Since we are using loopy belief propagation only to get the approximate .\ messages, we need not wait until loopy belief prop agation converges. ...
arXiv:1212.2507v1
fatcat:yex633pfjrgenamhwivxhdr5zi
Neural Enhanced Belief Propagation on Factor Graphs
[article]
2021
arXiv
pre-print
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We then propose a new hybrid model that runs conjointly a FG-GNN with belief propagation. ...
A traditional method to reason over these random variables is to perform inference using belief propagation. ...
in
Binary Markov Random Fields (Ising model) and the
performance is compared with Belief Propagation for
loopy graphs. ...
arXiv:2003.01998v5
fatcat:cmddnugmljcqtaimd7q5yxufb4
Importance sampling algorithms for Bayesian networks: Principles and performance
2006
Mathematical and computer modelling
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Jordan, Loopy belief propagation for approximate inference: An empirical study, in: 155-188]. ...
After that, we describe Evidence Pre-propagation Importance Sampling (EPIS-BN), an importance sampling algorithm that computes an importance function using two techniques: loopy belief propagation [K. ...
We also report the results of 200 iterations of loopy belief propagation. ...
doi:10.1016/j.mcm.2005.05.020
fatcat:ctgwj3d6ezfqhjbmwm6jhe7eam
Data association by loopy belief propagation
2010
2010 13th International Conference on Information Fusion
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propagation, can provide. ...
propagation can provide. ...
BP may be applied to loopy graphs (so-called loopy belief propagation). Practically, this simply involves repeated application of Eqs. ...
doi:10.1109/icif.2010.5711833
fatcat:du6bwdxavbdqvklg4fvdezbmdq
Hybrid generalized approximate message passing with applications to structured sparsity
2012
2012 IEEE International Symposium on Information Theory Proceedings
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The resulting algorithm, which we call hybrid generalized approximate message passing (Hybrid-GAMP), can yield significantly simpler implementations of sum-product and max-sum loopy belief propagation. ...
REVIEW OF LOOPY BELIEF PROPAGATION The sum-product loopy BP algorithm is based on iteratively passing estimates of the log marginals ∆ j (x j ) in (6) . ...
Message passing methods such as loopy belief propagation (BP) use this graphical structure to perform approximate inference or optimization in an iterative manner. ...
doi:10.1109/isit.2012.6283054
dblp:conf/isit/RanganFGS12
fatcat:sl2xubnyt5asdjw5zbfrqtp2be
Message Passing for Hybrid Bayesian Networks: Representation, Propagation, and Integration
2009
IEEE Transactions on Aerospace and Electronic Systems
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When a loop is present in the network, propagating messages are not exact, but the loopy algorithm usually converges and provides good approximate solutions. ...
The novelty of the work presented here is to propose a framework to compute, propagate, and integrate messages for hybrid models. ...
Generally, there are three main categories of approximate inference methods for BNs: model simplification, stochastic sampling, and loopy belief propagation. ...
doi:10.1109/taes.2009.5310315
fatcat:slqbvw4tlfce5c5gdlr7p2xeuq
Second-Order Semantic Dependency Parsing with End-to-End Neural Networks
2019
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
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We show that second-order parsing can be approximated using mean field (MF) variational inference or loopy belief propagation (LBP). ...
Loopy Belief Propagation We compare mean field variational inference and loopy belief propagation algorithms in Table 4 . ...
Loopy Belief Propagation Loopy belief propagation iteratively passes messages between variables and potential functions (factors). ...
doi:10.18653/v1/p19-1454
dblp:conf/acl/WangHT19
fatcat:k3vvow4jtzexzc3wgwissy4jvi
Second-Order Semantic Dependency Parsing with End-to-End Neural Networks
[article]
2021
arXiv
pre-print
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We show that second-order parsing can be approximated using mean field (MF) variational inference or loopy belief propagation (LBP). ...
Loopy Belief Propagation We compare mean field variational inference and loopy belief propagation algorithms in Table 4 . ...
Loopy Belief Propagation Loopy belief propagation iteratively passes messages between variables and potential functions (factors). ...
arXiv:1906.07880v3
fatcat:shypyuphu5abpgus24ljgrspvy
SPEDRE: a web server for estimating rate parameters for cell signaling dynamics in data-rich environments
2013
Nucleic Acids Research
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On termination, Loopy Belief Propagation provides optimized bins for all rate constants. ...
The main options are the number of bins for discretizing the parameter ranges, and the number of iterations for Loopy Belief Propagation. ...
doi:10.1093/nar/gkt459
pmid:23742908
pmcid:PMC3692124
fatcat:76yvxkphurfejffjg3i3ha6sai
Expectation Propagation for approximate Bayesian inference
[article]
2013
arXiv
pre-print
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Loopy belief propagation, because it propagates exact belief states, is useful for a limited class of belief networks, such as those which are purely discrete. ...
This method, "Expectation Propagation", unifies two previous techniques: assumed-density filtering, an extension of the Kalman filter, and loopy belief propagation, an extension of belief propagation in ...
SUMMARY This paper presented a generalization of belief propagation which is appropriate for hybrid belief networks. ...
arXiv:1301.2294v1
fatcat:yh6dx75vk5etnm47x2hvwnus3m
Strengthening Probabilistic Graphical Models: The Purge-and-merge Algorithm
2021
IEEE Access
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It is in principle possible to convert loopy PGMs to an equivalent tree structure, but this is usually impractical for interesting problems due to exponential blow-up. ...
However, while tree-structured PGMs always result in efficient and exact solutions, inference on graph (or loopy) structured PGMs is not guaranteed to discover the optimal solutions. ...
As such, PGM inference techniques such as loopy belief propagation [19] and loopy belief update [1] , [21] can be directly applied to these factor graphs.In order to perform belief propagation using ...
doi:10.1109/access.2021.3124760
fatcat:o34ljsx4sjgstlshglkhzsaff4
A Bayesian algorithm for distributed network localization using distance and direction data
[article]
2017
arXiv
pre-print
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The proposed MPHL is a distributed algorithm based on belief propagation (BP) and Markov chain Monte Carlo (MCMC) sampling. ...
This hybrid approach combines two sensing modalities to reduce the uncertainty in localizing the network nodes. ...
The MPHL runs a sum-product message passing algorithm over a loopy factor graph model, a variant of the loopy belief propagation (LBP) [24] , [25] . ...
arXiv:1704.01918v2
fatcat:lax6pmt7srbz7iiaz3t255hdxq
Autotagging music with conditional restricted Boltzmann machines
[article]
2011
arXiv
pre-print
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belief propagation. ...
belief propagation (LBP). ...
Loopy belief propagation [15] is a popular algorithm for approximating such marginals. Algorithm 3 details this procedure for the discriminative RBM. ...
arXiv:1103.2832v1
fatcat:yynwmustszhehczs4n3rae4wdq
Scalable Inference for Neuronal Connectivity from Calcium Imaging
[article]
2014
arXiv
pre-print
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In this work, we propose a computationally fast method for the state estimation based on a hybrid of loopy belief propagation and approximate message passing (AMP). ...
Using the structure, the updates in the proposed hybrid AMP methodology can be computed by a set of one-dimensional state estimation procedures and linear transforms with the connectivity matrix. ...
Using this factorization, approximate state estimation can then be efficiently performed via approximations of loopy belief propagation (BP) [15] . ...
arXiv:1409.0289v2
fatcat:o2apl4gjkvdt7h2itmlmedkzfq
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