undark-lab/swyft
A system for scientific simulation-based inference at scale.
This tool helps scientists efficiently analyze complex data from simulations to understand underlying physical parameters. You input simulated observational data and the parameters used to generate them, and it outputs insights into the most likely values of those parameters. This is ideal for researchers in fields like astrophysics or cosmology who work with expensive and time-consuming scientific simulations.
167 stars. No commits in the last 6 months. Available on PyPI.
Use this if you need to perform Bayesian or Frequentist inference for hundreds or thousands of parameters from complex scientific simulations, especially when those simulations are computationally expensive.
Not ideal if your simulations are simple, your data is not complex, or you require joint posterior estimation rather than efficient marginal posterior approximations.
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167
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17
Language
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License
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Last pushed
Mar 30, 2024
Commits (30d)
0
Dependencies
9
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