Abstract-We present a joint message passing approach that combines belief propagation and the mean field approximation. Our analysis is based on the region-based free energy approxi mation method proposed by Yedidia et al., which allows to use the same objective function (Kullback-Leibler divergence) as a starting point. In this method message passing fixed point equa tions (which correspond to the update rules in a message passing algorithm) are then obtained by imposing different region-based approximations and constraints on the mean field and belief propagation parts of the corresponding factor graph. Our results can be applied, for example, to algorithms that perform joint channel estimation and decoding in iterative receivers. This is...
We study the problem of joint channel estimation and detection in MIMO systems using belief propagat...
We propose a theoretical framework for non redundant reconstruction of a global loss from a collecti...
Many algorithms in signal processing and digital communications must deal with the problem of comput...
We present a joint message passing approach that combines belief propagation and the mean field appr...
We present a joint message passing approach that combines belief propagation and the mean field appr...
We design iterative receiver schemes for a generic communication system by treating channel estimati...
Merging belief propagation and the mean field approximation: A free energy approac
Important inference problems in statistical physics, computer vision, error-correcting coding theory...
Merging belief propagation and the mean field approximation: A free energy approac
Belief propagation (BP) was only supposed to work for tree-like networks but works surprisingly well...
Abstract—Inference problems in graphical models can be rep-resented as a constrained optimization of...
In this study, the authors investigate the use of combined belief propagation (BP), mean field (MF) ...
Over the last decade or so, Approximate Message Passing (AMP) algorithms have become extremely popul...
The chief aim of this paper is to propose mean-field approximations for a broad class of Belief ne...
We introduce a message passing belief propagation (BP) algorithm for factor graph over linear models...
We study the problem of joint channel estimation and detection in MIMO systems using belief propagat...
We propose a theoretical framework for non redundant reconstruction of a global loss from a collecti...
Many algorithms in signal processing and digital communications must deal with the problem of comput...
We present a joint message passing approach that combines belief propagation and the mean field appr...
We present a joint message passing approach that combines belief propagation and the mean field appr...
We design iterative receiver schemes for a generic communication system by treating channel estimati...
Merging belief propagation and the mean field approximation: A free energy approac
Important inference problems in statistical physics, computer vision, error-correcting coding theory...
Merging belief propagation and the mean field approximation: A free energy approac
Belief propagation (BP) was only supposed to work for tree-like networks but works surprisingly well...
Abstract—Inference problems in graphical models can be rep-resented as a constrained optimization of...
In this study, the authors investigate the use of combined belief propagation (BP), mean field (MF) ...
Over the last decade or so, Approximate Message Passing (AMP) algorithms have become extremely popul...
The chief aim of this paper is to propose mean-field approximations for a broad class of Belief ne...
We introduce a message passing belief propagation (BP) algorithm for factor graph over linear models...
We study the problem of joint channel estimation and detection in MIMO systems using belief propagat...
We propose a theoretical framework for non redundant reconstruction of a global loss from a collecti...
Many algorithms in signal processing and digital communications must deal with the problem of comput...