Symbolic-numeric algorithm for parameter estimation in discrete-time models with exp

Dynamic models describe phenomena across scientific disciplines, yet to make these models useful in application the unknown parameter values of the models must be determined. Discrete-time dynamic models are widely used to model biological processes, but it is often difficult to determine these para...

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Bibliographic Details
Published in:Journal of symbolic computation Vol. 128; p. 102387
Main Authors: Berman, Yosef, Forrest, Joshua, Grote, Matthew, Ovchinnikov, Alexey, Rueda, Sonia L.
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.05.2025
Subjects:
Discrete-time models
Flour beetle model
Parameter estimation
Symbolic-numeric computing
68W30
Parameter estimation
92B05
14Q20
13P15
Symbolic-numeric computing
39A60
Flour beetle model
Discrete-time models
ISSN:0747-7171
Online Access:Get full text
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Summary:Dynamic models describe phenomena across scientific disciplines, yet to make these models useful in application the unknown parameter values of the models must be determined. Discrete-time dynamic models are widely used to model biological processes, but it is often difficult to determine these parameters. In this paper, we propose a symbolic-numeric approach for parameter estimation in discrete-time models that involve univariate non-algebraic (locally) analytic functions such as exp. We illustrate the performance (precision) of our approach by applying our approach to two archetypal discrete-time models in biology (the flour beetle ‘LPA’ model and discrete Lotka-Volterra competition model). Unlike optimization-based methods, our algorithm guarantees to find all solutions of the parameter values up to a specified precision given time-series data for the measured variables provided that there are finitely many parameter values that fit the data and that the used polynomial system solver can find all roots of the associated polynomial system with interval coefficients.
ISSN:0747-7171
DOI:10.1016/j.jsc.2024.102387