sbi is a PyTorch package for simulation-based inference. Simulation-based inference is the process of finding the parameters of a simulator from observations.
sbi takes a Bayesian approach and returns 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.
sbi offers a simple interface for one-line posterior inference
from sbi inference import infer # import your simulator, define your prior over the parameters parameter_posterior = infer(simulator, prior, method='SNPE')
See below for the available methods of inference, SNPE, SNRE and SNLE.