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—The belief propagation (BP) or sum-product algorithm is a widely-used message-passing method for computing marginal distributions in graphical models. At the core of the BP message updates, when applied to a graphical model involving discrete variables with pairwise interactions, lies a matrix-vector product with complexity that is quadratic in the state(More)
—We introduce an efficient family of exact regenerating codes for data storage in large-scale distributed systems. We refer to these new codes as Distributed Replication-based Exact Simple Storage (DRESS) codes. A key property of DRESS codes is their very efficient distributed and uncoded repair and growth processes that have minimum bandwidth, reads and(More)
—The sum-product or belief propagation (BP) algorithm is widely used to compute exact or approximate marginals in graphical models. However, for graphical models with continuous or high-dimensional discrete states and/or high degree factors, it can be computationally expensive to update messages. We propose the stochastic belief propagation algorithm (SBP)(More)
The sum-product or belief propagation (BP) algorithm is a widely used message-passing technique for computing approximate marginals in graphical models. We introduce a new technique, called stochastic orthogonal series message-passing (SOSMP), for computing the BP fixed point in models with continuous random variables. It is based on a deterministic(More)
—Belief propagation (BP) is a widely used algorithm for computing the marginal distributions in graphical models. However, in applications involving continuous random variables, the messages themselves are real-valued functions, which leads to significant computational bottlenecks. In this paper, we propose a low complexity method for performing belief(More)
The problem of network-constrained averaging is to compute the average of a set of values distributed throughout a graph G using an algorithm that can pass messages only along graph edges. We study this problem in the noisy setting, in which the communication along each link is modeled by an additive white Gaussian noise channel. We propose a two-phase(More)
We propose a new method for estimating the mixing matrix, A, in the linear model x(t) = As(t), t = 1,. .. , T , for the problem of underdetermined Sparse Component Analysis (SCA). Contrary to most previous algorithms, there can be more than one dominant source at each instant (we call it a " multiple dominant " problem). The main idea is to convert the(More)
—The problem of network-constrained averaging is to compute the average of a collection of a set of values distributed throughout a network using an algorithm that can pass messages only along edges of the network. We study this problem in the noisy setting, in which the communication along each link is modeled by an additive white Gaussian noise channel.(More)
Low-density parity-check codes, a class of capacity-approaching linear codes, are particularly recognized for their efficient decoding scheme. The decoding scheme, known as the sum-product, is an iterative algorithm consisting of passing messages between variable and check nodes of the factor graph. The sum-product algorithm is fully parallelizable, owing(More)
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