Simulation-based inference toolkit
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Updated
Dec 23, 2024 - Python
Simulation-based inference toolkit
A Python library for amortized Bayesian workflows using generative neural networks.
Probabilistic Inference on Noisy Time Series
python Parameter EStimation TOolbox
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
Induction motor parameter estimation tool
Black-box Bayesian inference for agent-based models
Learning simulation parameters from experimental data, from the micro to the macro, from laptops to clusters.
Loop like a pro, make parameter studies fun.
A python package for Bayesian inference of gravitational-wave data
This is a disciplined Python implementation of the Recursive Least Squares Method
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.
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