Search - Belief Propagation

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[Bio-Recognize] icaML DL : 0: This a Bayesian ICA algorithm for the linear instantaneous mixing model with additive Gaussian noise [1]. The inference problem is solved by ML-II, i.e. the sources are found by integration over the source posterior and the noise covariance and mixing matrix are found by maximization of the marginal likelihood [1]. The sufficient statistics are estimated by either variational mean field theory with the linear response correction or by adaptive TAP mean field theory [2,3]. The mean field equations are solved by a belief propagation method [4] or sequential iteration. The computational complexity is N M^3, where N is the number of time samples and M the number of sources.
Update : 2025-02-17 Size : 7kb Publisher : 陈互
[Special Effects] TRW_S-v1.1 DL : 0: The two algorithms are max-product belief propagation (BP, Pearl 88) and sequential tree-reweighted max-product message passing (TRW-S, Kolmogorov 05)
Update : 2025-02-17 Size : 39kb Publisher : wdbigboy
[Special Effects] bp-vision DL : 0: This document briefly describes an implementation of the algorithms from our paper Efficient Belief Propagation for Early Vision which appeared in CVPR 2004.
Update : 2025-02-17 Size : 250kb Publisher : zky
[Communication] ldpcdecoder DL : 0: ldpc decode c程序 Iterative probabilistic decoding of linear block codes.Based on Pearl s Belief Propagation in Bayesian Networks.This version utilizes object to define graph nodes and LOG-LIKELIHOOD RATIOS. Much improved in terms of speed compared to log_pearl.c. It can use a look-up table to avoid exp() computations-ldpc decode c procedure Iterative probabilistic decoding of linear block codes.Based on Pearl s Belief Propagation in Bayesian Networks.This version utilizes object to define graph nodes and LOG-LIKELIHOOD RATIOS. Much improved in terms of speed compared to log_pearl.c.It can use a look-up table to avoid exp () computations
Update : 2025-02-17 Size : 4kb Publisher : sblkiaw
[Algorithm] c_inference_ver2.2 DL : 0: The package includes 3 Matlab-interfaces to the c-code: 1. inference.m An interface to the full inference package, includes several methods for approximate inference: Loopy Belief Propagation, Generalized Belief Propagation, Mean-Field approximation, and 4 monte-carlo sampling methods (Metropolis, Gibbs, Wolff, Swendsen-Wang). Use "help inference" from Matlab to see all options for usage. 2. gbp_preprocess.m and gbp.m These 2 interfaces split Generalized Belief Propagation into the pre-process stage (gbp_preprocess.m) and the inference stage (gbp.m), so the user may use only one of them, or changing some parameters in between. Use "help gbp_preprocess" and "help gbp" from Matlab. 3. simulatedAnnealing.m An interface to the simulated-annealing c-code. This code uses Metropolis sampling method, the same one used for inference. Use "help simulatedAnnealing" from Matlab.
Update : 2025-02-17 Size : 82kb Publisher : bevin
[Graph program] BP DL : 0: 基于可信传播的立体匹配算法(max-product belief propagation,accelerated updating)-Based on the credibility of the spread of stereo matching algorithm (max-product belief propagation, accelerated updating)
Update : 2025-02-17 Size : 2kb Publisher : 王拨
[matlab] gbp DL : 0: Gaussian belief propagation code in matlab.
Update : 2025-02-17 Size : 7kb Publisher : 王小剛
[Special Effects] bp-extensions DL : 0: 利用belief propagation方法实现的模型优化和参数的估计-Ways to achieve the use of belief propagation model optimization and parameter estimation
Update : 2025-02-17 Size : 26kb Publisher : 杨佳
[Communication] ldpc_c_code DL : 1: LDPC码在基于BP (Belief Propagation) 的迭代译码相结合的条件下具有逼近Shannon 限的性能，是继turbo 码后在纠错编码领域又一重大进展。压缩文件中给出了LDPC在高斯信道下的c程序。-LDPC codes based on BP (Belief Propagation) Iterative Decoding of combining conditions with performance approaching Shannon limit on the heels of turbo code error correction coding in another area of significant progress. Compressed file give the LDPC at Gaussian channel c under the procedure.
Update : 2025-02-17 Size : 7kb Publisher : aiguixia
[Special Effects] EfficientBeliefPropagationforEarlyVision DL : 0: 本文献是双目立体视觉全局算法：Belief Propagation中的经典文献，欢迎下载-The literature is the overall algorithm for binocular stereo vision: Belief Propagation in classic literature, please download
Update : 2025-02-17 Size : 248kb Publisher : shengjie wang
[Algorithm] BPalgorithm DL : 0: belief propagation algrithm
Update : 2025-02-17 Size : 1kb Publisher : leo
[Windows Develop] StandardBP DL : 0: This the program for stereo in the computer vision community. The program is made by utilizing the standard belief propagation algorithm. -This is the program for stereo in the computer vision community. The program is made by utilizing the standard belief propagation algorithm.
Update : 2025-02-17 Size : 5.87mb Publisher : Junseok Kwon
[matlab] BP DL : 0: Belief Propagation (BP) Implementations gabp.m, run_gabp.m => Gaussian BP - parallel version asynch_GBP.m => Gaussian BP - serial version sparse_gabp.m, run_sparse_gabp.m => Gaussian BP - sparse version, optimized, tested on sparse matrices gabpms.ms, run_gabpms.m => Quadratic Min-Sum algorithm - Moallemi and Van-Roy
Update : 2025-02-17 Size : 219kb Publisher : savsee
[matlab] LDLC DL : 0: Low density lattice decoder via non-parametric belief propagation
Update : 2025-02-17 Size : 4kb Publisher : savsee
[matlab] fault_detection DL : 0: Distributed fault identification via non-parametric belief propagation
Update : 2025-02-17 Size : 33kb Publisher : savsee
[matlab] NBP DL : 0: Nonparametric belief propagation implementation
Update : 2025-02-17 Size : 121kb Publisher : savsee
[Special Effects] bp-vision DL : 0: Efficient Belief Propagation for Early Vision实现代码-Efficient Belief Propagation for Early Vision
Update : 2025-02-17 Size : 250kb Publisher : 银石
[Documents] bp DL : 0: implementation of belief propagation algorithm in matlab
Update : 2025-02-17 Size : 216kb Publisher : pranay
[AI-NN-PR] LBP DL : 0: loopy belief propagation 算法-loopy belief propagation algorithm
Update : 2025-02-17 Size : 505kb Publisher : 陈蕾英
[Special Effects] Belief-Propagation DL : 0: 置信传播立体匹配算法，详细说明消息传递的过程，经典论文！-Markov random ?eld models provide a robust and uni?ed framework for early vision problems such as stereo and image restoration. Inference algorithms based on graph cuts and belief propagation have been found to yield accurate results, but despite recent advances are often too slow for practical use.
Update : 2025-02-17 Size : 185kb Publisher : mstar

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