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swyft

swyft is the official implementation of Truncated Marginal Neural Ratio Estimation, a hyper-efficient, simulation-based inference technique for complex data and expensive simulators.

5
contributors

Cite this software

What swyft can do for you

  • Estimates likelihood-to-evidence ratios for arbitrary marginal posteriors; they typically require fewer simulations than the corresponding joint.

  • Performs targeted inference by prior truncation, combining simulation efficiency with empirical testability.

  • seamlessly reuses simulations drawn from previous analyses, even with different priors.

  • integrates dask and zarr to make complex simulation easy.

swyft is designed to solve the Bayesian inverse problem when the user has access to a simulator that stochastically maps parameters to observational data. In scientific settings, a cost-benefit analysis often favors approximating the posterior marginality; swyft provides this functionality. The package additionally implements our prior truncation technique, routines to empirically test results by estimating the expected coverage, and a dask simulator manager with zarr storage to simplify use with complex simulators.

Keywords
No keywords avaliable
Programming languages
  • Jupyter Notebook 88%
  • Python 12%
License
  • Apache-2.0
</>Source code

Participating organisations

Netherlands eScience Center

Contributors

BM
Benjamin Kurt Miller
University of Amsterdam
CW
Christoph Weniger
University of Amsterdam
FN
Francesco Nattino
Netherlands eScience Center
Meiert Grootes
Meiert Grootes
Netherlands eScience Center
OK
Ou Ku
Netherlands eScience Center

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