BibTeX

@article{2305.15174v3,
Author        = {Cornelius Schröder and Jakob H. Macke},
Title         = {Simultaneous identification of models and parameters of scientific simulators},
Eprint        = {2305.15174v3},
ArchivePrefix = {arXiv},
PrimaryClass  = {cs.LG},
Abstract      = {Many scientific models are composed of multiple discrete components, and scientists often make heuristic decisions about which components to include. Bayesian inference provides a mathematical framework for systematically selecting model components, but defining prior distributions over model components and developing associated inference schemes has been challenging. We approach this problem in a simulation-based inference framework: We define model priors over candidate components and, from model simulations, train neural networks to infer joint probability distributions over both model components and associated parameters. Our method, simulation-based model inference (SBMI), represents distributions over model components as a conditional mixture of multivariate binary distributions in the Grassmann formalism. SBMI can be applied to any compositional stochastic simulator without requiring likelihood evaluations. We evaluate SBMI on a simple time series model and on two scientific models from neuroscience, and show that it can discover multiple data-consistent model configurations, and that it reveals non-identifiable model components and parameters. SBMI provides a powerful tool for data-driven scientific inquiry which will allow scientists to identify essential model components and make uncertainty-informed modelling decisions.},
Year          = {2023},
Month         = {May},
Url           = {http://arxiv.org/abs/2305.15174v3},
File          = {2305.15174v3.pdf}
}