swyft is the official implementation of Truncated Marginal Neural Ratio Estimation (TMNRE), a hyper-efficient, simulation-based inference technique for complex data and expensive simulators.
- Documentation & installation: https://swyft.readthedocs.io/en/latest/
- Example usage: https://swyft.readthedocs.io/en/latest/tutorial-notebooks.html
- Source code: https://github.com/undark-lab/swyft
- Support & discussion: https://github.com/undark-lab/swyft/discussions
- Bug reports: https://github.com/undark-lab/swyft/issues
- Contributing: https://swyft.readthedocs.io/en/latest/contributing-link.html
- Citation: https://swyft.readthedocs.io/en/latest/citation.html
swyft:
- 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.
- seamless 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.
- tmnre is the implementation of the paper Truncated Marginal Neural Ratio Estimation.
- v0.1.2 is the implementation of the paper Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious time.
- sbi is a collection of simulation-based inference methods. Unlike swyft, the repository does not include truncation nor marginal estimation of posteriors.