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https://github.com/undark-lab/swyft

A system for scientific simulation-based inference at scale.
https://github.com/undark-lab/swyft

likelihood-free-inference machine-learning marginal-neural-ratio-estimation neural-ratio-estimation parameter-estimation python pytorch simulation-based-inference truncated-neural-ratio-estimation

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A system for scientific simulation-based inference at scale.

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Swyft

=====



.. image:: https://raw.githubusercontent.com/undark-lab/swyft/v0.4.1/docs/source/_static/img/swyft_logo_wide.png

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*Swyft* is a system for scientific simulation-based inference at scale.



.. image:: https://badge.fury.io/py/swyft.svg

   :target: https://badge.fury.io/py/swyft

   :alt: PyPI version



.. .. image:: https://github.com/undark-lab/swyft/actions/workflows/tests.yml/badge.svg

..    :target: https://github.com/undark-lab/swyft/actions

..    :alt: Tests



.. .. image:: https://github.com/undark-lab/swyft/actions/workflows/syntax.yml/badge.svg

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..    :alt: Syntax



.. image:: https://codecov.io/gh/undark-lab/swyft/branch/master/graph/badge.svg?token=E253LRJWWE

   :target: https://codecov.io/gh/undark-lab/swyft

   :alt: codecov



.. .. image:: https://readthedocs.org/projects/swyft/badge/?version=latest

..    :target: https://swyft.readthedocs.io/en/latest/?badge=latest

..    :alt: Documentation Status



.. .. image:: https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat

..    :target: https://github.com/undark-lab/swyft/blob/master/CONTRIBUTING.md

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.. .. image:: https://colab.research.google.com/assets/colab-badge.svg

..    :target: https://colab.research.google.com/github/undark-lab/swyft/blob/master/notebooks/Quickstart.ipynb

..    :alt: colab



.. image:: https://joss.theoj.org/papers/10.21105/joss.04205/status.svg

   :target: https://doi.org/10.21105/joss.04205



.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.5752734.svg

   :target: https://doi.org/10.5281/zenodo.5752734



*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.



Swyft in action

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.. image:: https://raw.githubusercontent.com/undark-lab/swyft/v0.4.1/docs/source/_static/img/SBI-curve.gif

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* 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.