Nesting Particle Filters for Experimental Design in Dynamical Systems
<|reference_start|>Nesting Particle Filters for Experimental Design in Dynamical Systems: 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.<|reference_end|>
@article{iqbal2024nesting,
    title={Nesting Particle Filters for Experimental Design in Dynamical Systems},
    author={Sahel Iqbal, Adrien Corenflos, Simo S"arkk"a, Hany Abdulsamad},
    journal={arXiv preprint arXiv:2402.07868},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.07868},
    primaryClass={stat.ML cs.LG stat.ME}
}