PyHGF: A neural network library for predictive coding

The JAGS Module

causact: R package to accelerate computational Bayesian inference workflows in R through interactive visualization of models and their output.

This document contains Henry's study notes.

Bayesian inference with probabilistic programming.

Univariate Gaussian Mixture Model Neural Network Model (uGMM-NN)

High-performance reactive message-passing based Bayesian inference engine

CompiledKnowledge is a Python package for compiling and querying discrete probabilistic graphical models.

Visual web app to define & run inference on Bayesian networks with heatmap outputs

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).

This repo is a curated library to help you achieve a deeper understanding of what drives success and continuous improvement. Dive in, and discover content that can expand your thinking, sharpen your expertise, and fuel you drive better, whether you’re exploring new fields, honing in-demand skills, or simply looking for fresh perspectives.

Code for my PhD project on perceptual inference.

A novel semi-supervised framework combining contrastive learning with hierarchical probabilistic graphical models for remote sensing with limited labeled data. Our approach enhances CRFNet by learning discriminative representations from unlabeled imagery while capturing multi-resolution spatial dependencies.

Graph Variational Autoencoder on the MovieLens100K Dataset

This repo consists of the implementation of Gibbs sampling from scratch using the .net files with probabilities as input.

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

R package for inference in Bayesian networks.

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

Credici: Credal Inference for Causal Inference