Adaptive and robust experimental design for linear dynamical models using Kalman filter
| العنوان: | Adaptive and robust experimental design for linear dynamical models using Kalman filter |
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| المؤلفون: | Arno Strouwen, Bart M. Nicolaï, Peter Goos |
| المصدر: | Statistical papers Statistical Papers |
| بيانات النشر: | Springer Science and Business Media LLC, 2023. |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | Statistics and Probability, Statistics, Probability and Uncertainty, Mathematics |
| الوصف: | Current experimental design techniques for dynamical systems often only incorporate measurement noise, while dynamical systems also involve process noise. To construct experimental designs we need to quantify their information content. The Fisher information matrix is a popular tool to do so. Calculating the Fisher information matrix for linear dynamical systems with both process and measurement noise involves estimating the uncertain dynamical states using a Kalman filter. The Fisher information matrix, however, depends on the true but unknown model parameters. In this paper we combine two methods to solve this issue and develop a robust experimental design methodology. First, Bayesian experimental design averages the Fisher information matrix over a prior distribution of possible model parameter values. Second, adaptive experimental design allows for this information to be updated as measurements are being gathered. This updated information is then used to adapt the remainder of the design. |
| تدمد: | 1613-9798 0932-5026 |
| DOI: | 10.1007/s00362-023-01438-9 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7e20ee20f1b972d03b9329b73c06f076 https://doi.org/10.1007/s00362-023-01438-9 |
| Rights: | EMBARGO |
| رقم الانضمام: | edsair.doi.dedup.....7e20ee20f1b972d03b9329b73c06f076 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 16139798 09325026 |
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| DOI: | 10.1007/s00362-023-01438-9 |