Time-frequency decompositions: Bayesian model-based approaches

Journal Article

A range of developments in Bayesian time series modelling in recent years has focussed on issues of identifying latent structure in non-stationary time series, particularly driven by applications in which time-varying spectral structure of time series is an inherent and prime feature. This talk will review some of these developments, including the theoretical and methodological basis of decomposition methods in state-space models. The resulting methods can be viewed as providing a time-domain representation of changing spectral characteristics. Examples will be drawn from problems in clinical EEG studies, where the assessment of changes over time in frequency structure of components of EEG signals is key to characterizing brain seizures under various treatments.

Duke Authors

Cited Authors

  • West, M

Published Date

  • December 1, 1998

Published In

Volume / Issue

  • 1 /

Start / End Page

  • 276 -

International Standard Serial Number (ISSN)

  • 1058-6393

Citation Source

  • Scopus