We consider an Erdős-Rényi graph with n nodes and edge probability q that is embedded with a random subgraph of size K with edge probabilities p such that p > q. We address the problem of detecting the subgraph nodes when only the graph edges are observed, along with some extra knowledge of a small fraction of subgraph nodes, called cued vertices or cues. We employ a local and distributed algor...

The mystery of belief propagation (BP) decoder, especially of the turbo decoding, is studied from information geometrical viewpoint. The loopy belief network (BN) of turbo codes makes it difficult to obtain the true “belief” by BP, and the characteristics of the algorithm and its equilibrium are not clearly understood. Our study gives an intuitive understanding of the mechanism, and a new frame...

The empirical success of the belief propagation approximate inference algorithm has inspired numerous theoretical and algorithmic advances. Yet, for continuous non-Gaussian domains performing belief propagation remains a challenging task: recent innovations such as nonparametric or kernel belief propagation, while useful, come with a substantial computational cost and offer little theoretical g...

For exact inference, Belief Propagation(BP) on trees requires O(Td2) operations, where T is the number of variables and d is the cardinality of all hidden variables. This quadratic complexity becomes prohibitive when d is large. Stochastic Belief Propagation(SBP)[1] is an approximate inference algorithm which utilizes subtle changes to original BP in order to achieve O(1/ √ τ) error in O(τTd) t...

Stereo matching is one of the most active research areas in computer vision. In this paper, a novel stereo matching is proposed that utilizes Census measure and pixels-based intensity measure into data term of Belief Propagation algorithm. Traditional data term of Belief Propagation lies on pixels-based intensity measure, and its effect is not very well. We combine intensity and Census algorith...

This paper presents a novel communication-efficient parallel belief propagation (CE-PBP) algorithm for training latent Dirichlet allocation (LDA). Based on the synchronous belief propagation (BP) algorithm, we first develop a parallel belief propagation (PBP) algorithm on the parallel architecture. Because the extensive communication delay often causes a low efficiency of parallel topic modelin...

Belief Propagation (BP) can be very useful and efficient for performing approximate inference on graphs. But when the graph is very highly connected with strong conflicting interactions, BP tends to fail to converge. Generalized Belief Propagation (GBP) provides more accurate solutions on such graphs, by approximating Kikuchi free energies, but the clusters required for the Kikuchi approximatio...

Belief Propagation (BP) is one of the most popular methods for inference in probabilistic graphical models. BP is guaranteed to return the correct answer for tree structures, but can be incorrect or non-convergent for loopy graphical models. Recently, several new approximate inference algorithms based on cavity distribution have been proposed. These methods can account for the effect of loops b...

This paper proposes belief propagation based combined decoding scheme for low-density parity-check (LDPC)-coded orthogonal frequency-division multiplexing (OFDM) system with a peak-to-average power ratio (PAPR) reduction using the partial transmit sequence (PTS), which does not transmit PTS side information about the phase factors. Keywords— Belief Propagation (BP), Partial Transmit Sequence (P...

Compressive sensing theory has demonstrated that sparse signals can be recovered from a small number of random linear measurements. However, for practical purposes, like storage, transmission, or processing with modern digital equipment, continuous-valued compressive sensing measurements need to be quantized. In this thesis we examine the topic of optimal quantization of compressive sensing mea...