Spectral Density Estimation Of Stochastic Processes Under Missing Data And Uncertainty Quantification With Bayesian Deep Learning

authored by

Yu Chen, Edoardo Patelli, Benjamin Edwards, Michael Beer

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.

Organisation(s)

Institute for Risk and Reliability

External Organisation(s)

University of Liverpool
University of Strathclyde

Type

Contribution to book/anthology

Publication date

2023

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Computational Theory and Mathematics, Computer Science Applications, Modelling and Simulation, Statistics and Probability, Control and Optimization, Discrete Mathematics and Combinatorics

Electronic version(s)

https://doi.org/10.7712/120223.10371.19949 (Access: Open)