Köhs, L. ; Alt, B. ; Koeppl, H. (2021)
Variational Inference for Continuous-Time Switching Dynamical Systems.
35th Conference on Neural Information Processing Systems. virtual Conference (06.-14.12.2021)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Switching dynamical systems provide a powerful, interpretable modeling framework for inference in time-series data in, e.g., the natural sciences or engineering applications. Since many areas, such as biology or discrete-event systems, are naturally described in continuous time, we present a model based on a Markov jump process modulating a subordinated diffusion process. We provide the exact evolution equations for the prior and posterior marginal densities, the direct solutions of which are however computationally intractable. Therefore, we develop a new continuous-time variational inference algorithm, combining a Gaussian process approximation on the diffusion level with posterior inference for Markov jump processes. By minimizing the path-wise Kullback-Leibler divergence we obtain (i) Bayesian latent state estimates for arbitrary points on the real axis and(ii) point estimates of unknown system parameters, utilizing variational expectation maximization. We extensively evaluate our algorithm under the model assumption and for real-world examples.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Köhs, L. ; Alt, B. ; Koeppl, H. |
| Art des Eintrags: | Bibliographie |
| Titel: | Variational Inference for Continuous-Time Switching Dynamical Systems |
| Sprache: | Englisch |
| Publikationsjahr: | 29 September 2021 |
| Veranstaltungstitel: | 35th Conference on Neural Information Processing Systems |
| Veranstaltungsort: | virtual Conference |
| Veranstaltungsdatum: | 06.-14.12.2021 |
| URL / URN: | https://proceedings.neurips.cc/paper/2021/hash/abec16f483abb... |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Switching dynamical systems provide a powerful, interpretable modeling framework for inference in time-series data in, e.g., the natural sciences or engineering applications. Since many areas, such as biology or discrete-event systems, are naturally described in continuous time, we present a model based on a Markov jump process modulating a subordinated diffusion process. We provide the exact evolution equations for the prior and posterior marginal densities, the direct solutions of which are however computationally intractable. Therefore, we develop a new continuous-time variational inference algorithm, combining a Gaussian process approximation on the diffusion level with posterior inference for Markov jump processes. By minimizing the path-wise Kullback-Leibler divergence we obtain (i) Bayesian latent state estimates for arbitrary points on the real axis and(ii) point estimates of unknown system parameters, utilizing variational expectation maximization. We extensively evaluate our algorithm under the model assumption and for real-world examples. |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Bioinspirierte Kommunikationssysteme 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik DFG-Sonderforschungsbereiche (inkl. Transregio) DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > B: Adaptionsmechanismen DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > B: Adaptionsmechanismen > Teilprojekt B4: Planung |
| Hinterlegungsdatum: | 01 Okt 2021 10:15 |
| Letzte Änderung: | 30 Mai 2022 13:34 |
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