Nesting Particle Filters for Experimental Design in Dynamical Systems
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A4 Artikkeli konferenssijulkaisussa
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Date
2024
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en
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22
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Proceedings of Machine Learning Research, Volume 235, pp. 21047-21068
Abstract
In this paper, we propose a novel approach to Bayesian experimental design for nonexchangeable data that formulates it as risk-sensitive policy optimization. We develop the Inside-Out SMC2 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.Description
Publisher Copyright: Copyright 2024 by the author(s)
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Iqbal, S, Corenflos, A, Särkkä, S & Abdulsamad, H 2024, ' Nesting Particle Filters for Experimental Design in Dynamical Systems ', Proceedings of Machine Learning Research, vol. 235, pp. 21047-21068 . < https://proceedings.mlr.press/v235/iqbal24a.html >