We quantify the performance of approximations to stochastic filtering by the Kullback- Leibler divergence to the optimal Bayesian filter. Using a two-state Markov process that drives a Brownian measurement process as prototypical test case, we compare two stochastic filtering approximations: a static low-pass filter as baseline, and machine learning of Volterra expansions using nonlinear Vector Auto-Regression (nVAR). We highlight the crucial role of the chosen performance metric, and present two solutions to the specific challenge of predicting a likelihood bounded between 0 and 1.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:89184 |
| Date | 19 March 2024 |
| Creators | Ramakrishnan, Rahul O., Auconi, Andrea, Friedrich, Benjamin M. |
| Publisher | EDP Sciences |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:article, info:eu-repo/semantics/article, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 1286-4854, 31002, 10.1209/0295-5075/ac9d01, info:eu-repo/grantAgreement/Sächsischen Staatsministerium für Wissenschaft und Kunst/Forschungsprojektförderung Titelgruppe 70/100400118/ |
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