This book is concerned with the processing of signals that have been sam pled and digitized. The fundamental theory behind Digital Signal Process ing has been in existence for decades and has extensive applications to the fields of speech and data communications, biomedical engineering, acous tics, sonar, radar, seismology, oil exploration, instrumentation and audio signal processing to name but a few [87]. The term "Digital Signal Processing", in its broadest sense, could apply to any operation carried out on a finite set of measurements for whatever purpose. A book on signal processing would usually contain detailed de scriptions of the standard mathematical machinery often used to describe signals. It would also motivate an approach to real world problems based on concepts and results developed in linear systems theory, that make use of some rather interesting properties of the time and frequency domain representations of signals. While this book assumes some familiarity with traditional methods the emphasis is altogether quite different. The aim is to describe general methods for carrying out optimal signal processing.
Numerical Bayesian Methods Applied to Signal Processing
This thesis focuses on joint model order detection and estimation of the parameters of interest, with applications to narrowband and wideband array signal processing in both off-line and on-line contexts.
Presents the Bayesian approach to statistical signal processing for a variety of useful model sets This book aims to give readers a unified Bayesian treatment starting from the basics (Baye’s rule) to the more advanced (Monte Carlo ...
Estimation: Kalman H∞ and Nonlinear Approaches (Hoboken, P. Stavropoulos and D. Titterington, “Improved particle filters and smoothing,” in Sequential Monte Carlo Methods in Practice, edited by A. Doucet, N. de Freitas, and N. Gordon ...
The focus of this book is on "ill-posed inverse problems". These problems cannot be solved only on the basis of observed data. The building of solutions involves the recognition of other pieces of a priori information.
This is the first book-length treatment of the Variational Bayes (VB) approximation in signal processing.
9. J. Ruanaidh and W. Fitzgerald, Numerical Bayesian Methods Applied to Signal Processing, Springer, New York, 1996. 10. R. Neal, “Probabilistic inference using markov chain monte carlo methods,” Dept. of Computer Science, ...
One can then apply Bayesian model selection to determine the favoured number of sources by finding the model that maximizes ENcvr(NC). References Barreiro, R. B., Hobson, ... Numerical Bayesian Methods Applied to Signal Processing.
Ó Ruanaidh , J. J. K. and Fitzgerald , W. J. ( 1996 ) . Numerical Bayesian Methods Applied to Signal processing . New York : Springer . Proakis , J. , Deller , J. and Hansen , J. ( 1993 ) . Discrete - Time Processing of Speech Signals .
Bayesian inference in econometrics models using Monte Carlo integration. Econometrica, 57:1317-1339 ... Novel approach to nonlinear/non-Gaussian Bayesian state estimation. ... Numerical Bayesian methods applied to signal processing.