CITATION.cff

cff-version: 1.2.0
message: "Please cite https://doi.org/10.21105/joss.02505 also"
title: "sbi: Simulation-based inference toolkit"
authors:
- family-names: Tejero-Cantero
  given-names: Alvaro
  orcid: "http://orcid.org/0000-0002-8768-4227"
- family-names: Boelts
  given-names: Jan
  orcid: "http://orcid.org/0000-0003-4979-7092"
- family-names: Deistler
  given-names: Michael
  orcid: "http://orcid.org/0000-0002-3573-0404"
- family-names: Lueckmann
  given-names: Jan-Matthis
  orcid: "http://orcid.org/0000-0003-4320-4663"
- family-names: Durkan
  given-names: Conor
  orcid: "http://orcid.org/0000-0001-9333-7777"
- family-names: Gonçalves
  given-names: Pedro
  orcid: "http://orcid.org/0000-0002-6987-4836"
- family-names: Greenberg
  given-names: David
  orcid: "http://orcid.org/0000-0002-8515-0459"
- family-names: Macke
  given-names: Jakob
  orcid: "https://orcid.org/0000-0001-5154-8912"

version: 0.21.0
date-released: "2022-12-22"
identifiers:
 - type: "ascl-id"
   value: "2306.002"
 - type: "doi"
   value: 10.21105/joss.02505
 - type: "bibcode"
   value: "2023ascl.soft06002T"
abstract: "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>i.e.</i> useful for any observation) or focused (<i>i.e.</i>tailored to a particular observation), with different computational trade-offs. The code offers a simple interface for one-line posterior inference."
license: AGPL-3.0
repository-code: https://github.com/mackelab/sbi