| Issue |
EPL
Volume 140, Number 3, November 2022
|
|
|---|---|---|
| Article Number | 31002 | |
| Number of page(s) | 7 | |
| Section | Statistical physics and networks | |
| DOI | https://doi.org/10.1209/0295-5075/ac9d01 | |
| Published online | 17 November 2022 | |
Learning stochastic filtering
1 cfaed, Technische Universität Dresden - 01069 Dresden, Germany
2 Cluster of Excellence “Physics of Life” - 01307 Dresden, Germany
(a) E-mail: benjamin.m.friedrich@tu-dresden.de (corresponding author)
Received: 22 June 2022
Accepted: 24 October 2022
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.
© 2022 The author(s)
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