Abstract
Abstract In this paper, an overview of Bayesian methods and models in signal and image processing is given. The first part of the paper reviews some traditional classes of model employed for signal processing time series analysis. Marginal inference based upon analytic integration of hyperparameters is described for these models and illustrations are given for the problem of estimating sinusoidal frequency components in white Gaussian noise and for the general changepoint problem applied to digital communications. In the second part of the paper, state of the art applications are described which employ MCMC methods for the enhancement of noise-degraded audio signals, non-linear system identification and image sequence restoration. The complex modelling requirements and large datasets involved in these problems require sophisticated MCMC schemes employing efficient blocking schemes, model uncertainty strategies (both reversible jump and Gibbs variable selection) and non-linear/non-Gaussian models.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
