Bayesian Model Selection and Emulation for Protein Fluorescence

Ryan, W. , Husmeier, D. , Rolinski, O. J. and Vyshemirsky, V. (2023) Bayesian Model Selection and Emulation for Protein Fluorescence. In: 5th International Conference on Statistics: Theory and Applications (ICSTA'23), London, UK, 3-5 Aug 2023, ISBN 9781990800252 (doi: 10.11159/icsta23.153)

Abstract

Fluorescence decay of amino acids in protein is a complex process for which multiple models have been proposed. Likelihood function evaluation for certain models can be computationally expensive, and as such surrogate models may be introduced to speed up inference. In this paper, Gaussian processes are implemented in likelihood estimation of a range of models defined by convolutions of an initial excitation input and a decay function using both synthetic and real world data. Parameter inference and model selection using the surrogate models are performed and compared against the exact results. Model selection when incorporating surrogate models into the inference process is shown to be consistent.

• Item Type: Conference Proceedings
• Keywords: Protein fluorescence, Bayesian model selection, emulation, Kronecker product.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Ryan, William and Vyshemirsky, Dr Vladislav and Husmeier, Professor Dirk
• Authors: Ryan, W., Husmeier, D., Rolinski, O. J., and Vyshemirsky, V.
• College/School: College of Science and Engineering; College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Journal Name: Proceedings of the 5th International Conference on Statistics: Theory and Applications
• ISSN: 2562-7767
• ISBN: 9781990800252
• Copyright Holders: Copyright © 2023 International Aset Inc.
• First Published: First published in Proceedings of the 5th International Conference on Statistics: Theory and Applications (ICSTA'23), Paper No. 153.
• Publisher Policy: Reproduced with the permission of the publisher

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