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
Article, Bibliographie
This is the latest version of this item.
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.
| Item Type: | Article |
|---|---|
| Erschienen: | 2021 |
| Creators: | Ion, Ion Gabriel ; Wildner, C. ; Loukrezis, D. ; Koeppl, H. ; De Gersem, H. |
| Type of entry: | Bibliographie |
| Title: | Tensor-train approximation of the chemical master equation and its application for parameter inference |
| Language: | English |
| Date: | July 2021 |
| Publisher: | AIP Publishing |
| Journal or Publication Title: | The Journal of Chemical Physics |
| Volume of the journal: | 155 |
| Issue Number: | 3 |
| DOI: | 10.1063/5.0045521 |
| URL / URN: | https://aip.scitation.org/doi/full/10.1063/5.0045521 |
| Corresponding Links: | |
| 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. |
| Uncontrolled Keywords: | Numerical linear algebra, Tensor network theory, Bayesian inference, Stochastic processes, Probability theory, Algebraic operation, Chemical reaction dynamics |
| Identification Number: | Artikel-ID: 034102 |
| Divisions: | 18 Department of Electrical Engineering and Information Technology 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems 18 Department of Electrical Engineering and Information Technology > Institute for Accelerator Science and Electromagnetic Fields > Computational Electromagnetics 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications 18 Department of Electrical Engineering and Information Technology > Self-Organizing Systems Lab 18 Department of Electrical Engineering and Information Technology > Institute for Accelerator Science and Electromagnetic Fields > Electromagnetic Field Theory (until 31.12.2018 Computational Electromagnetics Laboratory) 18 Department of Electrical Engineering and Information Technology > Institute for Accelerator Science and Electromagnetic Fields Interdisziplinäre Forschungsprojekte Interdisziplinäre Forschungsprojekte > Centre for Synthetic Biology |
| Date Deposited: | 06 Sep 2021 07:11 |
| Last Modified: | 13 May 2024 11:23 |
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| Suche nach Titel in: | TUfind oder in Google |
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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) [Currently Displayed]
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