Nesting Particle Filters for Experimental Design in Dynamical Systems
Abstract
In this paper, we propose a novel approach to Bayesian experimental design for non-exchangeable data that formulates it as risk-sensitive policy optimization. We develop the Inside-Out SMC$^2$ algorithm, a nested sequential Monte Carlo technique to infer optimal designs, and embed it into a particle Markov chain Monte Carlo framework to perform gradient-based policy amortization. Our approach is distinct from other amortized experimental design techniques, as it does not rely on contrastive estimators. Numerical validation on a set of dynamical systems showcases the efficacy of our method in comparison to other state-of-the-art strategies.
Cite
Text
Iqbal et al. "Nesting Particle Filters for Experimental Design in Dynamical Systems." International Conference on Machine Learning, 2024.Markdown
[Iqbal et al. "Nesting Particle Filters for Experimental Design in Dynamical Systems." International Conference on Machine Learning, 2024.](https://mlanthology.org/icml/2024/iqbal2024icml-nesting/)BibTeX
@inproceedings{iqbal2024icml-nesting,
title = {{Nesting Particle Filters for Experimental Design in Dynamical Systems}},
author = {Iqbal, Sahel and Corenflos, Adrien and Särkkä, Simo and Abdulsamad, Hany},
booktitle = {International Conference on Machine Learning},
year = {2024},
pages = {21047-21068},
volume = {235},
url = {https://mlanthology.org/icml/2024/iqbal2024icml-nesting/}
}