BibTeX

@article{2502.08004v1,
Author        = {Vincent D. Zaballa and Elliot E. Hui},
Title         = {Optimizing Likelihoods via Mutual Information: Bridging Simulation-Based Inference and Bayesian Optimal Experimental Design},
Eprint        = {2502.08004v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ML},
Abstract      = {Simulation-based inference (SBI) is a method to perform inference on a variety of complex scientific models with challenging inference (inverse) problems. Bayesian Optimal Experimental Design (BOED) aims to efficiently use experimental resources to make better inferences. Various stochastic gradient-based BOED methods have been proposed as an alternative to Bayesian optimization and other experimental design heuristics to maximize information gain from an experiment. We demonstrate a link via mutual information bounds between SBI and stochastic gradient-based variational inference methods that permits BOED to be used in SBI applications as SBI-BOED. This link allows simultaneous optimization of experimental designs and optimization of amortized inference functions. We evaluate the pitfalls of naive design optimization using this method in a standard SBI task and demonstrate the utility of a well-chosen design distribution in BOED. We compare this approach on SBI-based models in real-world simulators in epidemiology and biology, showing notable improvements in inference.},
Year          = {2025},
Month         = {Feb},
Url           = {http://arxiv.org/abs/2502.08004v1},
File          = {2502.08004v1.pdf}
}