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Loopy Substructural Local Search for the Bayesian Optimization Algorithm [chapter]

Claudio F. Lima, Martin Pelikan, Fernando G. Lobo, David E. Goldberg
2009 Lecture Notes in Computer Science  
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]

Changhe Yuan, Marek J. Druzdzel
2012 arXiv   pre-print
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]

Victor Garcia Satorras, Max Welling
2021 arXiv   pre-print
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

Changhe Yuan, Marek J. Druzdzel
2006 Mathematical and computer modelling  
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

J L Williams, R A Lau
2010 2010 13th International Conference on Information Fusion  
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

Sundeep Rangan, Alyson K. Fletcher, Vivek K Goyal, Philip Schniter
2012 2012 IEEE International Symposium on Information Theory Proceedings  
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

Wei Sun, K.C. Chang
2009 IEEE Transactions on Aerospace and Electronic Systems  
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

Xinyu Wang, Jingxian Huang, Kewei Tu
2019 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics  
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]

Xinyu Wang, Jingxian Huang, Kewei Tu
2021 arXiv   pre-print
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

Tri Hieu Nim, Jacob K. White, Lisa Tucker-Kellogg
2013 Nucleic Acids Research  
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]

Thomas P. Minka
2013 arXiv   pre-print
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

Simon Streicher, Johan du Preez
2021 IEEE Access  
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]

Hassan Naseri, Visa Koivunen
2017 arXiv   pre-print
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]

Michael Mandel, Razvan Pascanu, Hugo Larochelle, Yoshua Bengio
2011 arXiv   pre-print
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]

Alyson K. Fletcher, Sundeep Rangan
2014 arXiv   pre-print
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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