Bayesian UQ for experimental data: Code
This code uses the PEtab problem (written in the yaml file) to perform MCMC sampling with pyPESTO. Afterwards an ensemble is created, that allows to compute the posterior predictive distribution and therefore the credibility intervals for the model output.
The sampling.py scipt of pyPESTO visulize was adjusted for an improved visualization. The used code was also uploaded.
A Conda environment file (.yml) containing the specific version of Python and installed packages with the according versions, can be used to avoid conflicts between package versions.
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Bayesian UQ for experimental data code.zip
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Created: 22nd Jul 2022 at 08:00
Last updated: 9th Dec 2022 at 06:08
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Version 2 (latest) Created 7th Dec 2022 at 16:08 by Sebastian Höpfl
Increased sample size to 200,000 on 4 parallel chains. Increased bounds of parameters to a uniform prior distribution in the interval [0, 100] for parameters A and B and [0,1000] for σ.
Version 1 (earliest) Created 22nd Jul 2022 at 08:00 by Sebastian Höpfl
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