Bayesian inference with probabilistic programming.
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Updated
Jun 9, 2024 - Julia
Bayesian inference with probabilistic programming.
The JAGS Module
Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
Inference of microbial interaction networks from large-scale heterogeneous abundance data
PyHGF: A neural network library for predictive coding
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
An(other) implementation of Explicit Duration Hidden Semi-Markov Models in Python 3
Implementation of various inference and learning algorithms for Probabilistic Graphical Models (PGMs) without off-the-shelf libraries. Also includes projects from the PGM specialization on Coursera offered by Stanford.
This repository is a mirror. If you want to raise an issue or contact us, we encourage you to do it on Gitlab (https://gitlab.com/agrumery/aGrUM).
Fast, flexible and easy to use probabilistic modelling in Python.
🚶Python Library for Random Walks
causact: R package to accelerate computational Bayesian inference workflows in R through interactive visualization of models and their output.
Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)
Type stable implementation of a Bayesian network.
⚗️ A curated list of Books, Research Papers, and Software for Bayesian Networks.
Blang's software development kit
Source code for the paper "Efficient Detection of Exchangeable Factors in Factor Graphs" (FLAIRS 2024)
High Performance Structured Prediction in PyTorch
Source code for the paper "Lifting Factor Graphs with Some Unknown Factors" (ECSQARU 2023)
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