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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 mixin
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loopy belief propagation 算法-loopy belief propagation algorithm
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Gaussian belief propagation code in matlab.
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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
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SIHO的置信传播迭代解码算法SPA，可以比较数据误码率-SIHO the belief propagation iterative decoding algorithm SPA, can compare the data error rate
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基于对数的置信度传播算法的LDPC译码程序，ldpc_decode.m
-Based on the number of belief propagation algorithm for LDPC decoding process, ldpc_decode.m
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基于Belief Propagation算法的立体匹配-Belief Propagation algorithm based on stereo matching
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Belief Propagation (BP) algorithm
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Implementing the Belief Propagation
Algorithm in MATLAB
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这是一个贝叶斯独立分量分析(ICA)算法的线性瞬时混合高斯噪声模型和添加剂。解决问题的是ML-II推论,即资源的整合在发现源后和噪声协方差矩阵和混合了最大化的边际似然。充分统计量的估计平均场或变分理论和线性响应修正或通过自适应平均场理论水龙头。平均场方程,解决了信仰传播法的或连续的迭代。-This is a bayesian independent component analysis (ICA) algorithm of instantaneous linear mixed gaussian
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基于置信传播算法和Viterbi检测的ISI信道估计，比较两种算法的性能-ISI channel is estimated based on the belief propagation algorithm and Viterbi detection, compare the performance of the algorithm
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很全置信度传播算法，对坐message passing的人很有用-Belief propagation algorithm
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用多尺度卡尔曼滤波法，对信号参数进行识别估计。高频信号和低频信号识别结合起来改进了算法识别的精确度和准确度。-It is an implementation of hierarchical (a.k.a. multi-scale) Kalman filter using belief propagation. The model parameters are estimated by expectation maximization (EM) algorithm. In this impleme
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一些关于置信度传播算法的论文资料，欢迎下载学习参考-Some papers on the belief propagation algorithm information, please download learning reference
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Belief propagation algorithm over the BSC channel
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置信传播即BP译码算法，在每一次迭代过程中,都要对全部比特和校验信息进行更新,存在计算量大、译码效率低的问题,故提出了单边传播信息的迭代BP算法-Belief Propagation that BP decoding algorithm, in each iteration, we must check for all the bits and update the information, there is a large amount of calculation, low coding e
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置信传播(belief propagation,BP)算法的计算复杂度较高，且变量节点和校验节点间信息传递的信息可靠，但是迭代的实现，就最大似然算法来说，验证其提高译码性能的特点。
-Belief propagation (belief propagation, BP) higher computational complexity of the algorithm, and reliable information between variable nodes and check node
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将置信传播(belief propagation,BP)算法从马尔科夫随机域的角度进行理解，
并通过变量节点和校验界定之间的迭代来实现信息传递，进而提高系统的误码率性能。
-The belief propagation (belief propagation, BP) algorithm is understood from the perspective of Markov random field, and by defining the iteration variable nod
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置信传播是一中很有效的算法，将其应用于检测，通过在检测与译码之间迭代交换传递信息，并有选择地通过单边消息传递来更新信息，降低系统实现的复杂度。 -Belief propagation is a very efficient algorithm to be applied to the detection, the information transmitted by the exchange between the iterative detection and decoding, and to
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置信传播算法可通过因子图的角度理解，也可通过马尔科夫随机域的思想来理解，不管从哪个角度实现，都可将其应用于检测，提高性能。-Belief propagation algorithm can be understood by the angle factor graph can also be understood by thinking of Markov random field, regardless of the angle from which to achieve, can be ap
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