Popular GitHub Repositories for Probabilistic Graphical Models

Fast, flexible and easy to use probabilistic modelling in Python.

This project has two parts. In part one, we use markov random field to denoise an image. In Part two, we use similar model for image segmentation.

High-performance reactive message-passing based Bayesian inference engine

scAR (single-cell Ambient Remover) is a deep learning model for removal of the ambient signals in droplet-based single cell omics

Official Repository of "Contextual Graph Markov Model" (ICML 2018 - JMLR 2020)

Splotch is a hierarchical generative probabilistic model for analyzing Spatial Transcriptomics (ST) data

A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions

Bayesian inference on wiring diagrams.

A python package for finding causal functional connectivity from neural time series observations.

My version of topic modelling using Latent Dirichlet Allocation (LDA) which finds the best number of topics for a set of documents using ldatuning package which comes with different metrics

[ICML 2024] Probabilistic Conceptual Explainers (PACE): Trustworthy Conceptual Explanations for Vision Foundation Models

A Tensorflow implementation of the paper https://arxiv.org/pdf/1803.07710.pdf

Probabilistic machine learning for reconstruction and parametrization of electronic band sturcture from photoemission spectroscopy data

Crema: Credal Models Algorithms

An(other) implementation of Explicit Duration Hidden Semi-Markov Models in Python 3

Gaussian Mixture Graphical Model Learning and Inference

Coordinate ascent mean-field variational inference (CAVI) using the evidence lower bound (ELBO) to iteratively perform the optimal variational factor distribution parameter updates for clustering.

Pytorch implementation of Variational Autoencoders - a popular deep generative probabilistic graphical model.

Portable Image Segmentation with Gibbs'

Subset of Data Variational Inference for Deep Gaussian Process Model