Abstract — The canonical problem of solving a system of linear equations arises in numerous contexts in information theory, com-munication theory, and related fields. In this contribution, we de-velop a solution based upon Gaussian belief propagation (GaBP) that does not involve direct matrix inversion. The iterative nature of our approach allows for a distributed message-passing implementation of the solution algorithm. We also address some properties of the GaBP solver, including convergence, exactness, its max-product version and relation to classical solution methods. The application example of decorrelation in CDMA is used to demonstrate the faster convergence rate of the proposed solver in comparison to conventional linear-algebraic i...
With the introduction of programmable graphical processing units (GPU) in the last decade, Heterogen...
Gaussian belief propagation (BP) is known to be an efficient message-passing algorithm for calculati...
Lifted inference approaches have rendered large, previously intractable probabilistic inference prob...
In this paper, the paradigm of linear detection is being reformulated as a Gaussian belief propagati...
We present an implementation-oriented algorithm for the recently developed Gaussian Belief Propagati...
Solving Linear Equation System (LESs) is a common problem in numerous fields of science. Even though...
We introduce a message passing belief propagation (BP) algorithm for factor graph over linear models...
Systems and control theory have found wide application in the analysis and design of numerical algor...
Gaussian belief propagation (GaBP) is an iterative algorithm for computing the mean (and variances) ...
Belief propagation (BP) on cyclic graphs is an efficient algorithm for computing approximate margina...
Abstract — In this work, we present a novel construction for solving the linear multiuser detection ...
Abstract—In order to compute the marginal probability density function (PDF) with Gaussian belief pr...
Abstract—We consider the estimation of an i.i.d. random vector observed through a linear transform f...
Despite of its wide success in many distributed statistical learning applications, the well-known Ga...
Distributed, iterative algorithms operating with minimal data structure while performing little comp...
With the introduction of programmable graphical processing units (GPU) in the last decade, Heterogen...
Gaussian belief propagation (BP) is known to be an efficient message-passing algorithm for calculati...
Lifted inference approaches have rendered large, previously intractable probabilistic inference prob...
In this paper, the paradigm of linear detection is being reformulated as a Gaussian belief propagati...
We present an implementation-oriented algorithm for the recently developed Gaussian Belief Propagati...
Solving Linear Equation System (LESs) is a common problem in numerous fields of science. Even though...
We introduce a message passing belief propagation (BP) algorithm for factor graph over linear models...
Systems and control theory have found wide application in the analysis and design of numerical algor...
Gaussian belief propagation (GaBP) is an iterative algorithm for computing the mean (and variances) ...
Belief propagation (BP) on cyclic graphs is an efficient algorithm for computing approximate margina...
Abstract — In this work, we present a novel construction for solving the linear multiuser detection ...
Abstract—In order to compute the marginal probability density function (PDF) with Gaussian belief pr...
Abstract—We consider the estimation of an i.i.d. random vector observed through a linear transform f...
Despite of its wide success in many distributed statistical learning applications, the well-known Ga...
Distributed, iterative algorithms operating with minimal data structure while performing little comp...
With the introduction of programmable graphical processing units (GPU) in the last decade, Heterogen...
Gaussian belief propagation (BP) is known to be an efficient message-passing algorithm for calculati...
Lifted inference approaches have rendered large, previously intractable probabilistic inference prob...