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.

• 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.