uncertainty-quantification

Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization

A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.

[JMLR 2026] "UQLM: A Python Package for Uncertainty Quantification in Large Language Models"

Lightweight, useful implementation of conformal prediction on real data.

Sensitivity Analysis Library in Python. Contains Sobol, Morris, FAST, and other methods.

A Library for Uncertainty Quantification.

Awesome-LLM-Robustness: a curated list of Uncertainty, Reliability and Robustness in Large Language Models

This repository contains a collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning models.

A Python-based toolbox of various methods in decision making, uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.

Literature survey, paper reviews, experimental setups and a collection of implementations for baselines methods for predictive uncertainty estimation in deep learning models.

Python package for conformal prediction

Open-source framework for uncertainty and deep learning models in PyTorch 🌱

Chaospy - Toolbox for performing uncertainty quantification.

A Python toolbox for conformal prediction research on deep learning models, using PyTorch.

👋 Puncc is a python library for predictive uncertainty quantification using conformal prediction.

UQpy (Uncertainty Quantification with python) is a general purpose Python toolbox for modeling uncertainty in physical and mathematical systems.

Probabilistic modelling and uncertainty quantification library

Next-generation camera-modeling toolkit

DeepHyper: A Python Package for Massively Parallel Hyperparameter Optimization in Machine Learning

Uncertainty Quantification 360 (UQ360) is an extensible open-source toolkit that can help you estimate, communicate and use uncertainty in machine learning model predictions.