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Simulation-based inference with the Python Package sbijax

S Dirmeier, S Ulzega, A Mira, C Albert - arXiv preprint arXiv:2409.19435, 2024 - arxiv.org
Computer Science paper cs.LG Suggest

… In the following, we will refer to the family of ABC methods and neural SBI methods as simulation-based inference. SBI methods can generally be divided into four classes …

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BibTeX

@article{2409.19435v1,
Author        = {Simon Dirmeier and Simone Ulzega and Antonietta Mira and Carlo Albert},
Title         = {Simulation-based inference with the Python Package sbijax},
Eprint        = {2409.19435v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {cs.LG},
Abstract      = {Neural simulation-based inference (SBI) describes an emerging family of
methods for Bayesian inference with intractable likelihood functions that use
neural networks as surrogate models. Here we introduce sbijax, a Python package
that implements a wide variety of state-of-the-art methods in neural
simulation-based inference using a user-friendly programming interface. sbijax
offers high-level functionality to quickly construct SBI estimators, and
compute and visualize posterior distributions with only a few lines of code. In
addition, the package provides functionality for conventional approximate
Bayesian computation, to compute model diagnostics, and to automatically
estimate summary statistics. By virtue of being entirely written in JAX, sbijax
is extremely computationally efficient, allowing rapid training of neural
networks and executing code automatically in parallel on both CPU and GPU.},
Year          = {2024},
Month         = {Sep},
Url           = {http://arxiv.org/abs/2409.19435v1},
File          = {2409.19435v1.pdf}
}

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