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

verfasst von

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.

Organisationseinheit(en)

Institut für Risiko und Zuverlässigkeit

Externe Organisation(en)

The University of Liverpool
University of Strathclyde

Typ

Beitrag in Buch/Sammelwerk

Publikationsdatum

2023

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Theoretische Informatik und Mathematik, Angewandte Informatik, Modellierung und Simulation, Statistik und Wahrscheinlichkeit, Steuerung und Optimierung, Diskrete Mathematik und Kombinatorik

Elektronische Version(en)

https://doi.org/10.7712/120223.10371.19949 (Zugang: Offen)