PyHGF: A neural network library for predictive coding
-
Updated
Jun 28, 2024 - Python
PyHGF: A neural network library for predictive coding
Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
Bayesian inference with probabilistic programming.
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).
High-performance reactive message-passing based Bayesian inference engine
The JAGS Module
Fast, flexible and easy to use probabilistic modelling in Python.
Statically typed probabilistic programming language, feat. GADT
Inference of microbial interaction networks from large-scale heterogeneous abundance data
Credici: Credal Inference for Causal Inference
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.
🚶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
Add a description, image, and links to the probabilistic-graphical-models topic page so that developers can more easily learn about it.
To associate your repository with the probabilistic-graphical-models topic, visit your repo's landing page and select "manage topics."