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Tensor-train approximation of the chemical master equation and its application for parameter inference.
- Source :
-
Journal of Chemical Physics . 7/21/2021, Vol. 155 Issue 3, p1-17. 17p. - Publication Year :
- 2021
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *CHEMICAL equations
*TENSOR products
*LINEAR systems
Subjects
Details
- Language :
- English
- ISSN :
- 00219606
- Volume :
- 155
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Journal of Chemical Physics
- Publication Type :
- Academic Journal
- Accession number :
- 151503770
- Full Text :
- https://doi.org/10.1063/5.0045521