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.

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1022 commits | Last update: March 10, 2022

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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.

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Programming Language
  • Python
License
  • Apache-2.0
Source code

Contributors

  • Christoph Weniger
    University of Amsterdam
  • Benjamin Kurt Miller
    University of Amsterdam
  • Meiert Grootes
    Netherlands eScience Center
  • Francesco Nattino
    Netherlands eScience Center
  • Ou Ku
    Netherlands eScience Center
Contact person
Christoph Weniger
University of Amsterdam

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