Ion, Ion Gabriel ; Wildner, C. ; Loukrezis, D. ; Koeppl, H. ; De Gersem, H. (2021)
Tensortrain 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 tensortrain format. The tensortrain approximation has been proven to be very efficient in representing highdimensional data arising from the explicit representation of the chemical master equation solution. An additional advantage of representing the probability mass function in the tensortrain 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 tensortrain method by performing inference tasks such as smoothing and parameter inference using the tensortrain 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 tensortrain 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:  Tensortrain 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 tensortrain format. The tensortrain approximation has been proven to be very efficient in representing highdimensional data arising from the explicit representation of the chemical master equation solution. An additional advantage of representing the probability mass function in the tensortrain 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 tensortrain method by performing inference tasks such as smoothing and parameter inference using the tensortrain 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 tensortrain 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:  ArtikelID: 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 > SelfOrganizing 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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Tensortrain approximation of the chemical master equation and its application for parameter inference. (deposited 30 Apr 2024 09:06)
 Tensortrain approximation of the chemical master equation and its application for parameter inference. (deposited 06 Sep 2021 07:11) [Currently Displayed]
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