sbi is a Python package for simulation-based inference, designed to meet the needs of both researchers and practitioners. Whether you need fine-grained control or an easy-to-use interface, sbi has you covered.

python Parameter EStimation TOolbox

Probabilistic Inference on Noisy Time Series

A library for using direct collocation in the optimization of dynamic systems.

Framework for dynamical system identification of floating-base rigid body tree structures

FMI-compliant Model Estimation in Python

Create Your Own Metropolis-Hastings Markov Chain Monte Carlo Algorithm for Bayesian Inference (With Python)

X-PSI: X-ray Pulse Simulation and Inference

A python-package for handling well based field campaigns.

A collection of mathematical models with experimental data in the PEtab format as benchmark problems in order to evaluate new and existing methodologies for data-based modelling

A Python Framework for Modeling and Analysis of Signaling Systems

Black-box Bayesian inference for agent-based models

Induction motor parameter estimation tool

Learning simulation parameters from experimental data, from the micro to the macro, from laptops to clusters.

This is a disciplined Python implementation of the Recursive Least Squares Method

Loop like a pro, make parameter studies fun.

A python package for Bayesian inference of gravitational-wave data

Python package for working with PEtab files

Parameter estimation for complex physical problems often suffers from finding ‘solutions’ that are not physically realistic. The PEUQSE software provides tools for finding physically realistic parameter estimates, graphs of the parameter estimate positions within parameter space, and plots of the final simulation results.

A simulation-based Inference (SBI) library designed to perform analysis on a wide class of gravitational wave signals