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[ascl:2306.002] sbi: Simulation-based inference toolkit

Simulation-based inference is the process of finding parameters of a simulator from observations. The PyTorch package sbi performs simulation-based inference by taking a Bayesian approach to return a full posterior distribution over the parameters, conditional on the observations. This posterior can be amortized (i.e. useful for any observation) or focused (i.e.tailored to a particular observation), with different computational trade-offs. The code offers a simple interface for one-line posterior inference.

Code site:
https://github.com/sbi-dev/sbi
Used in:
https://ui.adsabs.harvard.edu/abs/2023JCAP...04..010H
Described in:
https://ui.adsabs.harvard.edu/abs/2020JOSS....5.2505T
Bibcode:
2023ascl.soft06002T

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ascl:2306.002
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