CliMA/EnsembleKalmanProcesses.jl

Surrogate modeling and optimization for scientific machine learning (SciML)

Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning

distributed, likelihood-free inference

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

The core Dakota project

State estimation, smoothing and parameter estimation using Kalman and particle filters.

High-Performance Scientific Modeling with Julia and SciML

ELFI - Engine for Likelihood-Free Inference

Black-box optimization for Julia