Arşiv ve Dokümantasyon Merkezi
Dijital Arşivi

Bayesian methods for deconvolution of sparse processes

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dc.contributor Graduate Program in Electrical and Electronic Engineering.
dc.contributor.advisor Ertüzün, Ayşın.
dc.contributor.advisor Cemgil, Ali Taylan.
dc.contributor.author Yıldırım, Sinan.
dc.date.accessioned 2023-03-16T10:17:13Z
dc.date.available 2023-03-16T10:17:13Z
dc.date.issued 2009.
dc.identifier.other EE 2009 Y55
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/12728
dc.description.abstract In this work, various Bayesian methods for deconvolution and blind deconvolu- tion of sparse processes are studied. By using the prior assumption of sparsity, decon- volution and blind deconvolution operations are mapped to inference and parameter estimation methods in a Bayesian framework. For blind deconvolution of sparse processes, inverse-gamma model is proposed as a relaxation of the well known Bernoulli-Gaussian model. Methods based on expectation- maximization algorithm are investigated for both models, and several statistical infer- ence and parameter estimation techniques are presented for expectation and maximiza- tion steps. The improvement in the 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 deconvo- lution is demonstrated both by experiments on both simulated and real data. Also, in this way, the assumption of sparsity for receiver functions is validated by the obtained results. Finally, a preliminary theoretical solution to a challenging problem of blind es- timation of receiver function analysis is developed. The performances of proposed methods for the solution are tested on simulated data.
dc.format.extent 30cm.
dc.publisher Thesis (M.S.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2009.
dc.relation Includes appendices.
dc.relation Includes appendices.
dc.subject.lcsh Bayesian statistical decision theory .
dc.title Bayesian methods for deconvolution of sparse processes
dc.format.pages xiv, 87 leaves;


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