Engelmann, Nicolai ; Koeppl, Heinz (2022)
Forward-Backward Latent State Inference for Hidden Continuous-Time semi-Markov Chains.
36th Conference on Neural Information Processing Systems (NeurIPS 2022). New Orleans, USA (28.11.-09.12.2022)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Hidden semi-Markov Models (HSMM's)-while broadly in use-are restricted to a discrete and uniform time grid. They are thus not well suited to explain often irregularly spaced discrete event data from continuous-time phenomena. We show that non-sampling-based latent state inference used in HSMM's can be generalized to latent Continuous-Time semi-Markov Chains (CTSMC's). We formulate integro-differential forward and backward equations adjusted to the observation likelihood and introduce an exact integral equation for the Bayesian posterior marginals and a scalable Viterbi-type algorithm for posterior path estimates. The presented equations can be efficiently solved using well-known numerical methods. As a practical tool, variable-step HSMM's are introduced. We evaluate our approaches in latent state inference scenarios in comparison to classical HSMM's.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Engelmann, Nicolai ; Koeppl, Heinz |
| Art des Eintrags: | Bibliographie |
| Titel: | Forward-Backward Latent State Inference for Hidden Continuous-Time semi-Markov Chains |
| Sprache: | Englisch |
| Publikationsjahr: | 31 Oktober 2022 |
| Veranstaltungstitel: | 36th Conference on Neural Information Processing Systems (NeurIPS 2022) |
| Veranstaltungsort: | New Orleans, USA |
| Veranstaltungsdatum: | 28.11.-09.12.2022 |
| URL / URN: | https://openreview.net/forum?id=IRSyuxfYNb |
| Kurzbeschreibung (Abstract): | Hidden semi-Markov Models (HSMM's)-while broadly in use-are restricted to a discrete and uniform time grid. They are thus not well suited to explain often irregularly spaced discrete event data from continuous-time phenomena. We show that non-sampling-based latent state inference used in HSMM's can be generalized to latent Continuous-Time semi-Markov Chains (CTSMC's). We formulate integro-differential forward and backward equations adjusted to the observation likelihood and introduce an exact integral equation for the Bayesian posterior marginals and a scalable Viterbi-type algorithm for posterior path estimates. The presented equations can be efficiently solved using well-known numerical methods. As a practical tool, variable-step HSMM's are introduced. We evaluate our approaches in latent state inference scenarios in comparison to classical HSMM's. |
| Freie Schlagworte: | forward-backward, continuous time, hsmm, ctsmc, semi-Markov, latent state inference, sample-free posterior latent state inference in hidden continuous-time semi-Markov chains |
| 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 18 Fachbereich Elektrotechnik und Informationstechnik > Self-Organizing Systems Lab |
| Hinterlegungsdatum: | 04 Apr 2024 11:14 |
| Letzte Änderung: | 04 Apr 2024 11:19 |
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