Nesting Particle Filters for Experimental Design in Dynamical Systems
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866913367770267648 |
|---|---|
| author | Iqbal, Sahel Corenflos, Adrien Särkkä, Simo Abdulsamad, Hany |
| author_facet | Iqbal, Sahel Corenflos, Adrien Särkkä, Simo Abdulsamad, Hany |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_07868 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Nesting Particle Filters for Experimental Design in Dynamical Systems Iqbal, Sahel Corenflos, Adrien Särkkä, Simo Abdulsamad, Hany Machine Learning Methodology 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. |
| title | Nesting Particle Filters for Experimental Design in Dynamical Systems |
| topic | Machine Learning Methodology |
| url | https://arxiv.org/abs/2402.07868 |