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[ascl:2302.016] swyft: Scientific simulation-based inference at scale

swyft implements Truncated Marginal Neural Radio Estimation (TMNRE), a Bayesian parameter inference technique for complex simulation data. The code improves performance by estimating low-dimensional marginal posteriors rather than the joint posteriors of distributions, while also targeting simulations to targets of observational interest via an indicator function. The use of local amortization permits statistical checks, enabling validation of parameters that cannot be performed using sampling-based methods. swyft is also based on stochastic simulations, mapping parameters to observational data, and incorporates a simulator manager.

Code site:
https://github.com/undark-lab/swyft
Used in:
https://ui.adsabs.harvard.edu/abs/2022JCAP...09..004C
Described in:
https://ui.adsabs.harvard.edu/abs/2022JOSS....7.4205M
Bibcode:
2023ascl.soft02016M
Preferred citation method:

Please see citation information at https://swyft.readthedocs.io/en/latest/citation.html


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ascl:2302.016
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