Bayesian Deconvolution of Sparse Processes

ISBN-13: 9783844327533 / Angielski / Miękka / 2011 / 104 str.

Sinan Yildirim

Opis

Bayesian Deconvolution of Sparse Processes: In this work, various Bayesian methods for deconvolution and blind deconvolution of sparse processes are studied. For blind deconvolution of sparse processes, the inverse-gamma model is proposed as a relaxation of the well known Bernoulli-Gaussian model. Methods based on the expectation-maximization algorithm are investigated for both models, and several statistical inference and parameter estimation techniques are presented for expectation and maximization steps. The improvement in performance is demonstrated by experiments on simulated data. Receiver function analysis, a research topic in seismology, is studied as a real life application. Bayesian deconvolution is proposed as an alternative method to iterative deconvolution for estimating receiver functions. The superiority of Bayesian deconvolution is demonstrated by experiments on both simulated and real data. Finally, a preliminary theoretical solution to a challenging problem of blind estimation of receiver function analysis is developed. The performances of proposed methods for the solution are tested on simulated data.

O autorze

Sinan Y ld r m was born in Istanbul, Turkey, in 1984. He is currently a Ph.D. student in Statistical Laboratory, University of Cambridge, U.K. He received the B.S. and M.S. degrees in Electrical and Electronics engineering from Bogaziçi University, Istanbul, Turkey. His research interests are in the areas of Bayesian inference.