Ion, Ion Gabriel ; Wildner, C. ; Loukrezis, D. ; Koeppl, H. ; De Gersem, H. (2021)
Tensor-train approximation of the chemical master equation and its application for parameter inference.
In: The Journal of Chemical Physics, 155 (3)
doi: 10.1063/5.0045521
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (Abstract)
In this work, we perform Bayesian inference tasks for the chemical master equation in the tensor-train format. The tensor-train approximation has been proven to be very efficient in representing high-dimensional data arising from the explicit representation of the chemical master equation solution. An additional advantage of representing the probability mass function in the tensor-train format is that parametric dependency can be easily incorporated by introducing a tensor product basis expansion in the parameter space. Time is treated as an additional dimension of the tensor and a linear system is derived to solve the chemical master equation in time. We exemplify the tensor-train method by performing inference tasks such as smoothing and parameter inference using the tensor-train framework. A very high compression ratio is observed for storing the probability mass function of the solution. Since all linear algebra operations are performed in the tensor-train format, a significant reduction in the computational time is observed as well.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Ion, Ion Gabriel ; Wildner, C. ; Loukrezis, D. ; Koeppl, H. ; De Gersem, H. |
| Art des Eintrags: | Bibliographie |
| Titel: | Tensor-train approximation of the chemical master equation and its application for parameter inference |
| Sprache: | Englisch |
| Publikationsjahr: | Juli 2021 |
| Verlag: | AIP Publishing |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | The Journal of Chemical Physics |
| Jahrgang/Volume einer Zeitschrift: | 155 |
| (Heft-)Nummer: | 3 |
| DOI: | 10.1063/5.0045521 |
| URL / URN: | https://aip.scitation.org/doi/full/10.1063/5.0045521 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | In this work, we perform Bayesian inference tasks for the chemical master equation in the tensor-train format. The tensor-train approximation has been proven to be very efficient in representing high-dimensional data arising from the explicit representation of the chemical master equation solution. An additional advantage of representing the probability mass function in the tensor-train format is that parametric dependency can be easily incorporated by introducing a tensor product basis expansion in the parameter space. Time is treated as an additional dimension of the tensor and a linear system is derived to solve the chemical master equation in time. We exemplify the tensor-train method by performing inference tasks such as smoothing and parameter inference using the tensor-train framework. A very high compression ratio is observed for storing the probability mass function of the solution. Since all linear algebra operations are performed in the tensor-train format, a significant reduction in the computational time is observed as well. |
| Freie Schlagworte: | Numerical linear algebra, Tensor network theory, Bayesian inference, Stochastic processes, Probability theory, Algebraic operation, Chemical reaction dynamics |
| ID-Nummer: | Artikel-ID: 034102 |
| 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 Teilchenbeschleunigung und Theorie Elektromagnetische Felder > Computational Electromagnetics 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Self-Organizing Systems Lab 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Teilchenbeschleunigung und Theorie Elektromagnetische Felder > Theorie Elektromagnetischer Felder 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Teilchenbeschleunigung und Theorie Elektromagnetische Felder Interdisziplinäre Forschungsprojekte Interdisziplinäre Forschungsprojekte > Centre for Synthetic Biology |
| Hinterlegungsdatum: | 06 Sep 2021 07:11 |
| Letzte Änderung: | 13 Mai 2024 11:23 |
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Verfügbare Versionen dieses Eintrags
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Tensor-train approximation of the chemical master equation and its application for parameter inference. (deposited 30 Apr 2024 09:06)
- Tensor-train approximation of the chemical master equation and its application for parameter inference. (deposited 06 Sep 2021 07:11) [Gegenwärtig angezeigt]
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