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.

Please cite https://doi.org/10.21105/joss.02505 also