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Turbo decoding as an instance of Pearl’s belief propagation algorithm

by Robert J. Mceliece, David J. C. Mackay, Jung-fu Cheng - IEEE Journal on Selected Areas in Communications , 1998
"... Abstract—In this paper, we will describe the close connection between the now celebrated iterative turbo decoding algorithm of Berrou et al. and an algorithm that has been well known in the artificial intelligence community for a decade, but which is relatively unknown to information theorists: Pear ..."
Abstract - Cited by 404 (16 self) - Add to MetaCart
: Pearl’s belief propagation algorithm. We shall see that if Pearl’s algorithm is applied to the “belief network ” of a parallel concatenation of two or more codes, the turbo decoding algorithm immediately results. Unfortunately, however, this belief diagram has loops, and Pearl only proved that his

Constructing Free Energy Approximations and Generalized Belief Propagation Algorithms

by Jonathan S. Yedidia, William T. Freeman, Yair Weiss - IEEE Transactions on Information Theory , 2005
"... Important inference problems in statistical physics, computer vision, error-correcting coding theory, and artificial intelligence can all be reformulated as the computation of marginal probabilities on factor graphs. The belief propagation (BP) algorithm is an efficient way to solve these problems t ..."
Abstract - Cited by 585 (13 self) - Add to MetaCart
Important inference problems in statistical physics, computer vision, error-correcting coding theory, and artificial intelligence can all be reformulated as the computation of marginal probabilities on factor graphs. The belief propagation (BP) algorithm is an efficient way to solve these problems

Implementing the Belief Propagation Algorithm in MATLAB

by Björn S. Rüffer, Christopher M. Kellett , 2008
"... We provide some example Matlab code as a supplement to the paper [6]. This technical report is not intended as a standalone introduction to the belief propagation algorithm, but instead only aims to provide some technical material, which didn’t fit into the paper. 1 ..."
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We provide some example Matlab code as a supplement to the paper [6]. This technical report is not intended as a standalone introduction to the belief propagation algorithm, but instead only aims to provide some technical material, which didn’t fit into the paper. 1

I. Naïve Belief Propagation Algorithm

by unknown authors
"... One of the greatest abilities of the human eye is its capacity to perceive depth, an essential skill that allows us to perform fundamental tasks, such as avoiding obstacles and retrieving objects, as well as complicated tasks, such as driving a car. As advancements in the field of robotics allow rob ..."
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, introduced by Pedro Felzenszwalb of the University of Chicago, were applied to a naïve belief propagation algorithm to achieve more efficient belief propagation in depth finding. This paper provides an overview of the naïve belief propagation algorithm, the algorithm optimizations, and experimental results

The serial and parallel belief propagation algorithms

by Peng Hui Tan, Lars K. Rasmussen - in Proc. IEEE Intern. Symp. on Inform. Theory
"... Abstract — It has been shown that the stable fixed points of belief propagation (BP) algorithms correspond to extrema of the Bethe free energy. In this paper, we describe the dual problem for the minimization of the Bethe free energy and solve it using simple nonlinear block Gauss-Seidel and Jacobi ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
Abstract — It has been shown that the stable fixed points of belief propagation (BP) algorithms correspond to extrema of the Bethe free energy. In this paper, we describe the dual problem for the minimization of the Bethe free energy and solve it using simple nonlinear block Gauss-Seidel and Jacobi

The Role of Normalization in the Belief Propagation Algorithm∗

by Victorin Martin, Jean-marc Lasgouttes, Cyril Furtlehner, Inria Saclay , 2011
"... ar ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
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Quantum Belief Propagation Algorithm versus

by Farzad Ghafari, Jouneghani Mohammad, Babazadeh Davoud, Salami Hossein Movla
"... (will be inserted by the editor) ..."
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(will be inserted by the editor)

On Robust Stability of the Belief Propagation Algorithm for LDPC Decoding

by Björn S. Rüffer, Peter M. Dower, Christopher M. Kellett, Steven R. Weller
"... Abstract — The exact nonlinear loop gain of the belief propagation algorithm (BPA) in its log-likelihood ratio (LLR) formulation is computed. The nonlinear gains for regular lowdensity parity-check (LDPC) error correcting codes can be computed exactly using a simple formula. It is shown that in some ..."
Abstract - Cited by 2 (1 self) - Add to MetaCart
Abstract — The exact nonlinear loop gain of the belief propagation algorithm (BPA) in its log-likelihood ratio (LLR) formulation is computed. The nonlinear gains for regular lowdensity parity-check (LDPC) error correcting codes can be computed exactly using a simple formula. It is shown

On the Optimality of Solutions of the Max-Product Belief Propagation Algorithm in Arbitrary Graphs

by Yair Weiss, William T. Freeman , 2001
"... Graphical models, suchasBayesian networks and Markov random fields, represent statistical dependencies of variables by a graph. The max-product "belief propagation" algorithm is a local-message passing algorithm on this graph that is known to converge to a unique fixed point when the gra ..."
Abstract - Cited by 241 (13 self) - Add to MetaCart
Graphical models, suchasBayesian networks and Markov random fields, represent statistical dependencies of variables by a graph. The max-product "belief propagation" algorithm is a local-message passing algorithm on this graph that is known to converge to a unique fixed point when

On the Convergence of Belief Propagation Algorithm for Stochastic Networks With Loops

by Nobuyuki Taga Shigeru, Shigeru Mase , 2004
"... The belief propagation (BP) algorithm is a tool with which one can calculate beliefs, marginal probabilities, of stochastic networks without loops (e.g., Bayesian networks) in a time proportional to the number of nodes. For networks with loops, it may not converge and, even if it converges, beliefs ..."
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The belief propagation (BP) algorithm is a tool with which one can calculate beliefs, marginal probabilities, of stochastic networks without loops (e.g., Bayesian networks) in a time proportional to the number of nodes. For networks with loops, it may not converge and, even if it converges, beliefs
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