Simulation-based inference.
Project description
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.
sbi offers a simple interface for one-line posterior inference
from sbi inference import infer
# import your simulator, define your prior on the parameters
parameter_posterior = infer(simulator, prior, method='SNPE')
sbi is a community project. It is the PyTorch successor of
delfi, and started life as a fork of Conor M.
Durkan's lfi. Development is currently coordinated at the mackelab.
We would appreciate to hear how sbiis working for your simulation problems, and
welcome also bug reports, pull requests and any other feedback at
github.com/mackelab/sbi.
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