SPECTRAL DENSITY ESTIMATION OF STOCHASTIC PROCESSES UNDER MISSING DATA AND UNCERTAINTY QUANTIFICATION WITH BAYESIAN DEEP LEARNING

Chen, Y, Patelli, E, Edwards, B ORCID: 0000-0001-5648-8015 and Beer, M ORCID: 0000-0002-0611-0345 (2023) SPECTRAL DENSITY ESTIMATION OF STOCHASTIC PROCESSES UNDER MISSING DATA AND UNCERTAINTY QUANTIFICATION WITH BAYESIAN DEEP LEARNING. .

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Abstract

Stochastic processes are widely adopted in many domains to deal with problems which are stochastic in nature and involve strong nonlinearity, nonstationarity and uncertain system parameters. However, the uncertainties of spectral representation of the underlying stochastic processes have not been adequately acknowledged due to the data problems in practice, for instance, missing data. Therefore, this paper proposes a novel method for uncertainty quantification of spectral representation in the presence of missing data using Bayesian deep learning models. A range of missing levels are tested. An example in stochastic dynamics is employed for illustration.

Item Type:	Conference or Workshop Item (Unspecified)
Divisions:	Faculty of Science and Engineering > School of Engineering Faculty of Science and Engineering > School of Environmental Sciences
Depositing User:	Symplectic Admin
Date Deposited:	17 Jan 2024 12:10
Last Modified:	17 Jan 2024 12:10
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3177884