Köhs, L. ; Alt, B. ; Koeppl, H. (2022)
Markov Chain Monte Carlo for Continuous-Time Switching Dynamical Systems.
39th International Conference on Machine Learning. Baltimore, USA (17.-23.07.2022)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Switching dynamical systems are an expressive model class for the analysis of time-series data. As in many fields within the natural and engineering sciences, the systems under study typically evolve continuously in time, it is natural to consider continuous-time model formulations consisting of switching stochastic differential equations governed by an underlying Markov jump process. Inference in these types of models is however notoriously difficult, and tractable computational schemes are rare. In this work, we propose a novel inference algorithm utilizing a Markov Chain Monte Carlo approach. The presented Gibbs sampler allows to efficiently obtain samples from the exact continuous-time posterior processes. Our framework naturally enables Bayesian parameter estimation, and we also include an estimate for the diffusion covariance, which is oftentimes assumed fixed in stochastic differential equation models. We evaluate our framework under the modeling assumption and compare it against an existing variational inference approach.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Köhs, L. ; Alt, B. ; Koeppl, H. |
| Art des Eintrags: | Bibliographie |
| Titel: | Markov Chain Monte Carlo for Continuous-Time Switching Dynamical Systems |
| Sprache: | Englisch |
| Publikationsjahr: | 18 Mai 2022 |
| Veranstaltungstitel: | 39th International Conference on Machine Learning |
| Veranstaltungsort: | Baltimore, USA |
| Veranstaltungsdatum: | 17.-23.07.2022 |
| URL / URN: | https://proceedings.mlr.press/v162/kohs22a.html |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Switching dynamical systems are an expressive model class for the analysis of time-series data. As in many fields within the natural and engineering sciences, the systems under study typically evolve continuously in time, it is natural to consider continuous-time model formulations consisting of switching stochastic differential equations governed by an underlying Markov jump process. Inference in these types of models is however notoriously difficult, and tractable computational schemes are rare. In this work, we propose a novel inference algorithm utilizing a Markov Chain Monte Carlo approach. The presented Gibbs sampler allows to efficiently obtain samples from the exact continuous-time posterior processes. Our framework naturally enables Bayesian parameter estimation, and we also include an estimate for the diffusion covariance, which is oftentimes assumed fixed in stochastic differential equation models. We evaluate our framework under the modeling assumption and compare it against an existing variational inference approach. |
| 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: | 09 Jun 2022 09:18 |
| Letzte Änderung: | 02 Nov 2022 13:44 |
| PPN: | 501045155 |
| Zugehörige Links: | |
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