Comparative analysis of the -pinene case: predictive regions for x2 obtained from a 9-point real dataset.

Left: predictive intervals using three different methodologies: (CUQDyn1, CUQDyn2) and STAN. Right: the predictive region using CUQDyn1. Numerical details are available in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013098#sec006" target="_b...

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Bibliographic Details
Main Author:	Alberto Portela (21349980) (author)
Other Authors:	Julio R. Banga (8131953) (author), Marcos Matabuena (6984305) (author)
Published:	2025
Subjects:	Medicine Biotechnology Computational Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified several illustrative scenarios posing significant challenges computational model predictions complex biological systems reliable uncertainty calibration robust uq enables conventional bayesian methods bayesian statistical methods dynamic biological systems art uq approaches bayesian methods systems biology uncertainty quantification dynamic models systematically determining system dynamics seeking rapid scale models provide non predictive tasks predictive models parameter sensitivities parameter distributions often require many state improving robustness deeper understanding critical due conformal prediction conformal algorithms computationally expensive asymptotic guarantees
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Comparative analysis of the -pinene case: predictive regions for <i>x</i><sub>2</sub> obtained from a 9-point real dataset.

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