A parallel belief propagation algorithm based on Particle Filtering (PF) for channel estimation and Low-Density Parity-Check (LDPC) decoding is presented in this paper based on Compute Unified Device Architecture (CUDA). The authors have found that compared with the traditional Belief Propagation (BP) algorithm with fixed estimated noise power, BP algorithm based on PF [1] not only gives a good...

We formulate a Belief Propagation (BP) algorithm in the context of the capacitated minimum-cost network flow problem (MCF). Unlike most of the instances of BP studied in the past, the messages of BP in the context of this problem are piecewise-linear functions. We prove that BP converges to the optimal solution in pseudo-polynomial time, provided that the optimal solution is unique and the prob...

In this paper, we propose a modified disparity estimation method using belief propagation (BP) for view interpolation. The view interpolation method generates an arbitrary intermediate view image with disparity. To extract disparities, we modified the BP based stereo matching algorith which solves the problem using Bayesian belief propagation. We propose two methods to improve accuracy of dispa...

In this paper we propose an algorithm for approximate inference on graphical models based on belief propagation (BP). Our algorithm is an approximate version of Cutset Conditioning, in which a subset of variables is instantiated to make the rest of the graph singly connected. We relax the constraint of single-connectedness, and select variables one at a time for conditioning, running belief pro...

Computing the partition function (i.e., the normalizing constant) of a given pairwise binary graphical model is NP-hard in general. As a result, the partition function is typically estimated by approximate inference algorithms such as belief propagation (BP) and tree-reweighted belief propagation (TRBP). The former provides reasonable estimates in practice but has convergence issues. The later ...

This paper introduces a notational frame to characterize the four basic product-based Message Passing (MP) heuristics currently available for SAT: Belief Propagation (BP), Survey Propagation (SP), Expectation Maximization BP Global (EMBPG) and Expectation Maximization SP Global (EMSPG). Using this framework, the paper introduces indirect structural interpolation (ISI). Using this technique, we ...

Survey propagation (SP) is a message passing procedure that attempts to model all the fixed points of Belief Propagation (BP), thereby improving BP’s approximation in loopy graphs where BP’s assumptions do not hold. For this, SP messages represent distributions over BP messages. Unfortunately this requirement makes SP intractable beyond constraint satisfaction problems because, to perform gener...

Abstract In order to solve the high latency problem of polar codes belief propagation decoding algorithm in 5G and dimension limitation under deep learning, a multilayer perceptron (MLP-BP) based on partitioning idea is proposed. this work, decoded using neural networks partitioning, right transfer message value BP also set complete process. Simulation results show that, compared with algorithm...

Belief propagation (BP) is well-known as a low complexity decoding algorithm with strong performance for important classes of quantum error correcting codes, e.g. notably the low-density parity check (LDPC) code class random expander codes. However, it also that BP breaks down when facing topological codes such surface code, where naive fails entirely to reach below-threshold regime, i.e. regim...

In his seminal paper, Pearl [1986] introduced the notion of Bayesian networks and the first processing algorithm, Belief Propagation (BP), that computes posterior marginals, called beliefs, for each variable when the network is singly connected. The paper provided the foundation for the whole area of Bayesian networks. It was the first in a series of influential papers by Pearl, his students an...