Overview and implementation of Belief Propagation and Loopy Belief Propagation algorithms: sum-product, max-product, max-sum
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Updated
Sep 10, 2019 - Jupyter Notebook
Overview and implementation of Belief Propagation and Loopy Belief Propagation algorithms: sum-product, max-product, max-sum
Clique recycling non-Gaussian (multi-modal) factor graph solver; also see Caesar.jl.
factor graph library
Implementation of simple block matching, block matching with dynamic programming and Stereo Matching using Belief Propagation algorithm for stereo disparity estimation
Slides and code for the Morgan Claypool book on "Individual and Collective Graph Mining: Principles, Algorithms and Applications"
Loopy belief propagation for factor graphs on discrete variables in JAX
Belief propagation with sparse matrices (scipy.sparse) in Python for LDPC codes. Includes NumPy implementation of message passing (min-sum and sum-product) and a few other decoders.
The algorithm solves the DC state estimation problem in electric power systems using the Gaussian belief propagation over factor graphs.
PyHGF: A neural network library for predictive coding
Linear Gaussian Bayesian Networks - Inference, Parameter Learning and Representation. 🖧
Implementation of the Belief Propagation Side Channel Attack
Gaussian belief propagation solver for noisy linear systems with real coefficients and variables.
Focusing Belief Propagation on Commitee machines with binary weights
The FactorGraph package provides the set of different functions to perform inference over the factor graph with continuous or discrete random variables using the belief propagation algorithm.
Belief propagation on Tanner graphs (LDPC decoder)
Repository contains the derivation of Belief Propagation algorithm from the ground up, as well as generic Java implementation of the Loopy Belief Propagation algorithm.
Conditional Associative Logic Memory
Matrix Product Belief Propagation
Belief-propagation receivers for nonlinear OFDM
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