Parameter estimation for allometric trophic network models : A variational Bayesian inverse problem approach
Tirronen, M., & Kuparinen, A. (2024). Parameter estimation for allometric trophic network models : A variational Bayesian inverse problem approach. Methods in Ecology and Evolution, Early online. https://doi.org/10.1111/2041-210x.14447
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Methods in Ecology and EvolutionDate
2024Copyright
© 2024 The Author(s). Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society.
Differential equation models are powerful tools for predicting biological systems, capable of projecting far into the future and incorporating data recorded at arbitrary times. However, estimating these models' parameters from observations can be challenging because numerical methods are often required to approximate their solution. An example of such a model is the allometric trophic network model, for which studies considering its inverse problem are limited, particularly in the Bayesian framework. Here we develop a variational Bayesian method for parameter inference of the allometric trophic network model and explore how accurately we can recover its parameter values. We represent the model as a Bayesian neural network, which combines an artificial neural network with Bayesian inference, using a surrogate for the posterior distribution of model parameters, and train this model by evolutionary optimization to avoid potentially costly computation of the gradient with respect to the model parameters. Using synthetic data, we compare the accuracy of this variational inference to ordinary least squares estimation. To reduce the number of estimated parameters, we focus on the inference of functional response parameters. Our variational Bayesian method yields parameter estimates that are comparable to the ordinary least squares results in terms of accuracy. The method provides a promising approach for including uncertainty quantification in parameter estimation, which the simple ordinary least squares approach as it is does not address. Regardless of the method, potential multimodality of the inference problem is nonetheless important to keep in mind. The present study suggests a technique for parameter inference of ordinary differential equation models in the Bayesian context. We propose the method especially for validation of the allometric trophic network model against empirical data.
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John Wiley & SonsISSN Search the Publication Forum
2041-210XKeywords
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https://converis.jyu.fi/converis/portal/detail/Publication/243982293
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European Commission; Research Council of FinlandFunding program(s)
CSA Coordination and Support Action, H2020; ERC Consolidator Grant; Academy Project, AoF
The content of the publication reflects only the author’s view. The funder is not responsible for any use that may be made of the information it contains.
Additional information about funding
This study was funded by the Research Council of Finland (project grant 317495 to AK). This project has also received funding from the European Union’s Horizon 2020 Research and Innovation programme under grant agreement No 951963. The project has received funding also from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 770884). ...License
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