https://doi.org/10.1140/epjb/s10051-021-00053-7
Regular Article - Computational Methods
Exploration of nonlinear parallel heterogeneous reaction pathways through Bayesian variable selection
1
Research Institute for Marine Geodynamics, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), 236-0061, Yokosuka, Japan
2
PRESTO, Japan Science and Technology Agency (JST), 332-0012, Kawaguchi, Japan
3
Graduate School of Engineering, Kobe University, 1-1 Rokkodai-cho, Nada-ku, 657-8501, Kobe, Japan
4
Center for Mathematical and Data Sciences, Kobe University, 1-1 Rokkodai-cho, Nada-ku, 657-8501, Kobe, Japan
Received:
19
August
2020
Accepted:
15
January
2021
Published online:
2
February
2021
Inversion is a key method for extracting nonlinear dynamics governed by heterogeneous reaction that occur in parallel in the natural sciences. Therefore, in this study, we propose a Bayesian statistical framework to determine the active reaction pathways using only the noisy observable spatial distribution of the solid phase. In this method, active reaction pathways were explored using a Widely Applicable Bayesian Information Criterion (WBIC), which is used to select models within the framework of Bayesian inference. Plausible reaction mechanisms were determined by maximizing the posterior distribution. This conditional probability is obtained through Markov chain Monte Carlo simulations. The efficiency of the proposed method is then determined using simulated spatial data of the solid phase. The results show that active reaction pathways can be identified from the redundant candidates of reaction pathways. After these redundant reaction pathways were excluded, the controlling factor of the reaction dynamics was estimated with high accuracy.
© The Author(s) 2021
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