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.

Papers using Swyft/TMNRE

2021

• “Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation“ Cole+ https://arxiv.org/abs/2111.08030

2022

• “Estimating the warm dark matter mass from strong lensing images with truncated marginal neural ratio estimation” Anau Montel+, https://arxiv.org/abs/2205.09126

• “SICRET: Supernova Ia Cosmology with truncated marginal neural Ratio EsTimation” Karchev+ https://arxiv.org/abs/2209.06733

• “One never walks alone: the effect of the perturber population on subhalo measurements in strong gravitational lenses” Coogan+ https://arxiv.org/abs/2209.09918

• “Detection is truncation: studying source populations with truncated marginal neural ratio estimation” Anau Montel+ https://arxiv.org/abs/2211.04291

2023

• “Debiasing Standard Siren Inference of the Hubble Constant with Marginal Neural Ratio Estimation” Gagnon-Hartman+ https://arxiv.org/abs/2301.05241

• “Constraining the X-ray heating and reionization using 21-cm power spectra with Marginal Neural Ratio Estimation” Saxena+ https://arxiv.org/abs/2303.07339

• “Peregrine: Sequential simulation-based inference for gravitational wave signals”, Bhardwaj+ https://arxiv.org/abs/2304.02035

• “Albatross: A scalable simulation-based inference pipeline for analysing stellar streams in the Milky Way”, Alvey+ https://arxiv.org/abs/2304.02032

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

• As of v0.4.0, Swyft is based on pytorch-lightning, with a completely updated

• 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.

Relevant packages

• sbi is a collection of simulation-based inference methods. Unlike Swyft, the repository does not include our truncation scheme nor marginal estimation of posteriors.

• lampe is an implementation of amoritzed simulation-based inference methods aimed at simulation-based inference researchers due to its flexibility.