probabilistic-inference

Python Library for Causal and Probabilistic Modeling using Bayesian Networks

Bayesian inference with probabilistic programming.

ProbLog is a Probabilistic Logic Programming Language for logic programs with probabilities.

a python framework to build, learn and reason about probabilistic circuits and tensor networks

Probabilistic Circuits from the Juice library

Probabilistic programming system for fast and exact symbolic inference

Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)

Code for the paper Iterated Denoising Energy Matching for Sampling from Boltzmann Densities.

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).

[ICML 2025] Official implementation of "AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting"

A scalable and accurate probabilistic network configuration analyzer verifying network properties in the face of random failures.

This repo demonstrates how to build a surrogate (proxy) model by multivariate regressing building energy consumption data (univariate and multivariate) and use (1) Bayesian framework, (2) Pyomo package, (3) Genetic algorithm with local search, and (4) Pymoo package to find optimum design parameters and minimum energy consumption.

Pure julia implementation of Multiple Affine Invariant Sampling for efficient Approximate Bayesian Computation

An official repository for tutorials of Probabilistic Modelling and Reasoning (2023/2024) - a University of Edinburgh master's course.

PyTorch implementation for "Long Horizon Temperature Scaling", ICML 2023

Arithmetic coding library with statistical models like PPM and Context mixing (demonstrating core principles of probabilistic inference, ensemble learning in AI)

ComBiNet: Compact Convolutional Bayesian Neural Network for Image Segmentation

Solutions for the Projects of the Artificial Intelligence (CS 188) course of UC Berkeley

Combined Learning from Demonstration and Motion Planning