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 approximation method proposed by Yedidia et al. We show that the message passing fixed-point equations obtained with this combination correspond to stationary points of a constrained region-based free energy approximation. Moreover, we present a convergent implementation of these message passing fixed-point equations provided that the underlying factor graph fulfills certain technical conditions. In addition, we show how to include hard constraints in the part of the factor graph corresponding to belief propagation. Finally, we demonstrate an application of our method to iterati...
An important part of problems in statistical physics and computer science can be expressed as the co...
Neuronal computations rely upon local interactions across synapses. For a neuronal network to perfor...
AbstractInference in Boltzmann machines is NP-hard in general. As a result approximations are often ...
We present a joint message passing approach that combines belief propagation and the mean field appr...
Abstract-We present a joint message passing approach that combines belief propagation and the mean f...
We design iterative receiver schemes for a generic communication system by treating channel estimati...
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 letter, a message-passing algorithm that combines belief propagation and expectation propaga...
Accurate evaluation of Bayesian model evidence for a given data set is a fundamental problem in mode...
We propose a novel iterative estimation algorithm for linear observation models called S-AMP. The fi...
International audienceA number of problems in statistical physics and computer science can be expres...
When belief propagation (BP) converges, it does so to a stationary point of the Bethe free energy F,...
An important part of problems in statistical physics and computer science can be expressed as the co...
Neuronal computations rely upon local interactions across synapses. For a neuronal network to perfor...
AbstractInference in Boltzmann machines is NP-hard in general. As a result approximations are often ...
We present a joint message passing approach that combines belief propagation and the mean field appr...
Abstract-We present a joint message passing approach that combines belief propagation and the mean f...
We design iterative receiver schemes for a generic communication system by treating channel estimati...
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 letter, a message-passing algorithm that combines belief propagation and expectation propaga...
Accurate evaluation of Bayesian model evidence for a given data set is a fundamental problem in mode...
We propose a novel iterative estimation algorithm for linear observation models called S-AMP. The fi...
International audienceA number of problems in statistical physics and computer science can be expres...
When belief propagation (BP) converges, it does so to a stationary point of the Bethe free energy F,...
An important part of problems in statistical physics and computer science can be expressed as the co...
Neuronal computations rely upon local interactions across synapses. For a neuronal network to perfor...
AbstractInference in Boltzmann machines is NP-hard in general. As a result approximations are often ...