swyft 0.4.1

Universal scalable simulation-based inference with TMNRE (Truncated Marginal Neural Ratio Estimation) and pytorch-lightning.

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. As of v0.4.0, swyft is based on pytorch-lightning.

Swyft in action

• Swyft makes it convenient to perform Bayesian or Frequentist inference of hundreds, thousands or millions of parameter posteriors by constructing optimal data summaries.

• To this end, Swyft estimates likelihood-to-evidence ratios for arbitrary marginal posteriors; they typically require fewer simulations than the corresponding joint.

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

• Swyft is based on stochastic simulators, which map parameters stochastically to observational data. Swyft makes it convenient to define such simulators as graphical models.

• 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 simulator manager with zarr storage to simplify use with complex simulators.

Further information

• Documentation & installation: https://swyft.readthedocs.io/

• 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 history

• v0.3.2 is the version that was submitted to JOSS.

• 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.