This paper concerns message passing based approaches to sparse Bayesian learning (SBL) with a linear model corrupted by additive white Gaussian noise with unknown variance. With the conventional factor graph, mean field (MF) message passing based algorithms have been proposed in the literature. In this work, instead of using the conventional factor graph, we modify the factor graph by adding so...

Belief Propagation (BP) is a widely used approximation for exact probabilistic inference in graphical models, such as Markov Random Fields (MRFs). In graphs with cycles, however, no exact convergence guarantees for BP are known, in general. For the case when all edges in the MRF carry the same symmetric, doubly stochastic potential, recent works have proposed to approximate BP by linearizing th...

Latent Dirichlet allocation (LDA) is an important hierarchical Bayesian model for probabilistic topic modeling, which attracts worldwide interests and touches on many important applications in text mining, computer vision and computational biology. This paper introduces a topic modeling toolbox (TMBP) based on the belief propagation (BP) algorithms. TMBP toolbox is implemented by MEX C++/Matlab...

Low-density parity-check (LDPC propagation (BP) algorithm achieve a remar close to the Shannon limit at reasonable de Conventionally, each iteration in decoding p steps, the horizontal step and the vertical ste efficient implementation of the adaptive offse algorithm for decoding LDPC codes using the is proposed. Furthermore, the performan algorithm compared with belief-propagation investigated...

The max-product Belief Propagation (BP) is a popular message-passing heuristic for approximating a maximum-a-posteriori (MAP) assignment in a joint distribution represented by a graphical model (GM). In the past years, it has been shown that BP can solve a few classes of Linear Programming (LP) formulations to combinatorial optimization problems including maximum weight matching, shortest path ...

It is known that fixed points of loopy belief propagation (BP) correspond to stationary points of the Bethe variational problem, where we minimize the Bethe free energy subject to normalization and marginalization constraints. Unfortunately, this does not entirely explain BP because BP is a dual rather than primal algorithm to solve the Bethe variational problem – beliefs are infeasible before ...

We propose four finite-length scaling laws to predict the frame error rate (FER) performance in waterfall region of spatially-coupled low-density parity-check code ensembles under full belief propagation (BP) decoding with a limit on number iterations and law for sliding window decoding, also limited iterations. The BP provide choice between accuracy computational complexity; good balance them ...

In this work, with combined belief propagation (BP), mean field (MF) and expectation propagation (EP), an iterative receiver is designed for joint phase noise (PN) estimation, equalization and decoding in a coded communication system. The presence of the PN results in a nonlinear observation model. Conventionally, the nonlinear model is directly linearized by using the first-order Taylor approx...

An original approach to multi-objective optimization is introduced, using a message-passing algorithm to sample the Pareto set, i.e. the set of Pareto-nondominated solutions. Several heuristics are proposed and tested on a simple biobjective 3-SAT problem. The first one is based on a straightforward deformation of the Survey-Propagation (SP) equation to locally encode a Pareto trade-off. A simp...

In this paper, we have developed several concepts such as the tree concept, short cycle concept and group shuffling of a propagation to decrypt low-density parity-check (LDPC) codes. Thus, proposed an algorithm based on where probability occurrence takes exponential form factor appearance belief propagation-group shuffled (EFAP-GSBP). This is used for wireless communication applications by prov...