swyft 0.3.1

Truncated Marginal Neural Ratio Estimation with an inhomogeneous poisson point process cache.

swyft

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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 <https://dask.org/>_ and zarr <https://zarr.readthedocs.io/en/stable/>_ 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 <https://dask.org/>_ simulator manager with zarr <https://zarr.readthedocs.io/en/stable/>_ storage to simplify use with complex simulators.

Related

• tmnre <https://github.com/bkmi/tmnre>_ is the implementation of the paper Truncated Marginal Neural Ratio Estimation <https://arxiv.org/abs/2107.01214>_.
• v0.1.2 <https://github.com/undark-lab/swyft/releases/tag/v0.1.2>_ is the implementation of the paper Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious time <https://arxiv.org/abs/2011.13951>_.
• sbi <https://github.com/mackelab/sbi>_ is a collection of simulation-based inference methods. Unlike swyft, the repository does not include truncation nor marginal estimation of posteriors.