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

Clique recycling non-Gaussian (multi-modal) factor graph solver; also see Caesar.jl.

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

PGM

Sum Data Table dengan Flutter

This is a project in which exact inference for tree-structured graph and approximate inference for general graph is implemented.

This repository contains Python codes for Autoenncoder, Sparse-autoencoder, HMM, Expectation-Maximization, Sum-product Algorithm, ANN, Disparity map, PCA.

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

Compute a moving sum of products incrementally.

Compute a sum of products incrementally.