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Tensor-train approximation of the chemical master equation and its application for parameter inference

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 (034102)
doi: 10.1063/5.0045521
Artikel, Bibliographie

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
Titel der Zeitschrift, Zeitung oder Schriftenreihe: The Journal of Chemical Physics
Jahrgang/Volume einer Zeitschrift: 155
(Heft-)Nummer: 034102
DOI: 10.1063/5.0045521
URL / URN: https://aip.scitation.org/doi/full/10.1063/5.0045521
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
Zusätzliche Informationen:

034102 Artikelnummer

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
Hinterlegungsdatum: 06 Sep 2021 07:11
Letzte Änderung: 26 Nov 2021 11:03
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