BibTeX

@article{2411.17337v2,
Author        = {Jan Boelts and Michael Deistler and Manuel Gloeckler and Álvaro Tejero-Cantero and Jan-Matthis Lueckmann and Guy Moss and Peter Steinbach and Thomas Moreau and Fabio Muratore and Julia Linhart and Conor Durkan and Julius Vetter and Benjamin Kurt Miller and Maternus Herold and Abolfazl Ziaeemehr and Matthijs Pals and Theo Gruner and Sebastian Bischoff and Nastya Krouglova and Richard Gao and Janne K. Lappalainen and Bálint Mucsányi and Felix Pei and Auguste Schulz and Zinovia Stefanidi and Pedro Rodrigues and Cornelius Schröder and Faried Abu Zaid and Jonas Beck and Jaivardhan Kapoor and David S. Greenberg and Pedro J. Gonçalves and Jakob H. Macke},
Title         = {sbi reloaded: a toolkit for simulation-based inference workflows},
Eprint        = {2411.17337v2},
ArchivePrefix = {arXiv},
PrimaryClass  = {cs.LG},
Abstract      = {Scientists and engineers use simulators to model empirically observed
phenomena. However, tuning the parameters of a simulator to ensure its outputs
match observed data presents a significant challenge. Simulation-based
inference (SBI) addresses this by enabling Bayesian inference for simulators,
identifying parameters that match observed data and align with prior knowledge.
Unlike traditional Bayesian inference, SBI only needs access to simulations
from the model and does not require evaluations of the likelihood function. In
addition, SBI algorithms do not require gradients through the simulator, allow
for massive parallelization of simulations, and can perform inference for
different observations without further simulations or training, thereby
amortizing inference. Over the past years, we have developed, maintained, and
extended sbi, a PyTorch-based package that implements Bayesian SBI algorithms
based on neural networks. The sbi toolkit implements a wide range of inference
methods, neural network architectures, sampling methods, and diagnostic tools.
In addition, it provides well-tested default settings, but also offers
flexibility to fully customize every step of the simulation-based inference
workflow. Taken together, the sbi toolkit enables scientists and engineers to
apply state-of-the-art SBI methods to black-box simulators, opening up new
possibilities for aligning simulations with empirically observed data.},
Year          = {2024},
Month         = {Nov},
Url           = {http://arxiv.org/abs/2411.17337v2},
File          = {2411.17337v2.pdf}
}