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Description: 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.
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Author: 陈互 |
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Description: 本文以此为背景,提出了基于事件驱动的BDI agent实现体系结构,用信念(Belief)、事件(Event)、规划(Plan)等内部特征来描述软件agent,并给出了面向对象层次上的软件agent的UML模型,该模型定义了构成软件agent的四个对象:Agent、BeliefSet、Event、Plan 为描述这些对象及其交互关系,本文以java语言为基础,扩展出了能描述这四个对象的java类以及描述其交互关系-this background paper, based on Event-Driven Implementation BDI agent architecture, with conviction (Belief), event (Event) plan (Plan) to describe the characteristics of the internal software agent, and given the level of object-oriented software agent UML model, The model defines a software agent constitute the four targets : Agent, BeliefSet. Event, Plan to describe these objects and their mutual relationship, this paper-based java language, can be expanded with a description of the four types of java object and describe their mutual relationship
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Author: 王浩(jiqixueyuan) |
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Description: The two algorithms are max-product belief propagation (BP, Pearl 88) and
sequential tree-reweighted max-product message passing (TRW-S, Kolmogorov 05)
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Author: wdbigboy |
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Description: This document briefly describes an implementation of the algorithms from
our paper Efficient Belief Propagation for Early Vision which appeared
in CVPR 2004.
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Author: zky |
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Description: 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
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Author: sblkiaw |
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Description: 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.
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Author: bevin |
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Description: 使用 c++ 写的图像处理平台 ，支持 belief propogation, dynamic programming,graphic cut. 等算法 ，在vs2005 下调试通过 ，如果 不会使用，请发邮件 walwj@sohu.com 咨询-Use c++ Write image processing platform, to support the belief propogation, dynamic programming, graphic cut. Such as algorithms, under the debugger in VS2005 is passed, if not used, please email walwj@sohu.com Advisory
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Author: david |
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Description: The algorithm is based on “Segment-Based Stereo Matching Using Belief Propogation and a Self-Adapting Dissimilarity Measure” by Klaus, Sormann, and Karner.
-The algorithm is based on
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Size: 468992 |
Author: 徐亮 |
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Description: 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)
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Author: 王拨 |
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Description: Gaussian belief propagation code in matlab.
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Size: 7168 |
Author: 王小剛 |
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Description: 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
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Size: 26624 |
Author: 杨佳 |
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Description: 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.
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Author: aiguixia |
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Description: 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
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Size: 253952 |
Author: shengjie wang |
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Description: belief propagation algrithm
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Author: leo |
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Description: 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.
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Author: Junseok Kwon |
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Description: 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
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Author: savsee |
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Description: Low density lattice decoder via non-parametric belief propagation
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Author: savsee |
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Description: Distributed fault identification via non-parametric belief propagation
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Author: savsee |
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Description: 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.
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Size: 189440 |
Author: mstar |
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Description: 利用深度置信网络进行回归建模(Regression Modeling Using Deep Belief Network)
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Size: 7168 |
Author: 蕉下客 |
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