Factor graphs and loopy belief propagation implemented in Python

Overview and implementation of Belief Propagation and Loopy Belief Propagation algorithms: sum-product, max-product, max-sum

Playing with Low-density parity-check codes

Hybrid (non)-Gaussian solver for Distributed Factor Graphs, with clique recycling; and is officially supported as part of the NavAbility(TM) toolchain and Caesar.jl ecosystem.

factor graph library

Implementation of AAAI 21 paper: Nested Named Entity Recognition with Partially Observed TreeCRFs

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 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.

LDPC MATLAB simulation using BPSK + AWGN modulation decoded using Sum Product and Min Sum Algorithm

Gaussian belief propagation solver for noisy linear systems with real coefficients and variables.

An implementation of the Loopy Belief Propagation algorithm using CUDA

Clean Factor Graphs in Python

AVX implementation of different LDPC decoders MS NMS SCMS SCSP under floor/layer schedulers

MATLAB implementations of Loopy Belief Propagation (LBP) for stereo matching, featuring Sum-Product, Max-Product and Min-Sum message-passing algorithms for disparity estimation.

Special Project - QCLDPC (2019 Spring)

Sum Data Table dengan Flutter

PGM

Matlab implementation of Sum-product algorithm for analyzing the behavior of the S&P 500 index over a period of time.

Compute a sum of products incrementally.

Compute a moving sum of products incrementally.