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Distance-learning for approximate Bayesian computation to model a volcanic eruption
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Pacchiardi, Lorenzo, Künzli, Pierre, Chopard, Bastien, Schöngens, Marcel and Dutta, Ritabrata (2021) Distance-learning for approximate Bayesian computation to model a volcanic eruption. Sankhya B, 83 (1). pp. 288-317. doi:10.1007/s13571-019-00208-8 ISSN 0976-8394.
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Official URL: https://doi.org/10.1007/s13571-019-00208-8
Abstract
Approximate Bayesian computation (ABC) provides us with a way to infer parameters of models, for which the likelihood function is not available, from an observation. Using ABC, which depends on many simulations from the considered model, we develop an inferential framework to learn parameters of a stochastic numerical simulator of volcanic eruption. Moreover, the model itself is parallelized using Message Passing Interface (MPI). Thus, we develop a nested-parallelized MPI communicator to handle the expensive numerical model with ABC algorithms. ABC usually relies on summary statistics of the data in order to measure the discrepancy model output and observation. However, informative summary statistics cannot be found for the considered model. We therefore develop a technique to learn a distance between model outputs based on deep metric-learning. We use this framework to learn the plume characteristics (eg. initial plume velocity) of the volcanic eruption from the tephra deposits collected by field-work associated with the 2450 BP Pululagua (Ecuador) volcanic eruption.
| Item Type: | Journal Article | ||||||
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| Subjects: | Q Science > QA Mathematics Q Science > QE Geology |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||
| SWORD Depositor: | Library Publications Router | ||||||
| Library of Congress Subject Headings (LCSH): | Volcanic eruptions -- Mathematical models, Bayesian statistical decision theory, Mathematical analysis | ||||||
| Journal or Publication Title: | Sankhya B | ||||||
| Publisher: | Springer India | ||||||
| ISSN: | 0976-8394 | ||||||
| Official Date: | May 2021 | ||||||
| Dates: |
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| Volume: | 83 | ||||||
| Number: | 1 | ||||||
| Page Range: | pp. 288-317 | ||||||
| DOI: | 10.1007/s13571-019-00208-8 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
| Date of first compliant deposit: | 14 April 2022 | ||||||
| Date of first compliant Open Access: | 14 April 2022 | ||||||
| RIOXX Funder/Project Grant: |
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