Arşiv ve Dokümantasyon Merkezi
Dijital Arşivi

Sequential Bayesian modeling of non-stationary non-Gaussian processes

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dc.contributor Ph.D. Program in Electrical and Electronic Engineering.
dc.contributor.advisor Ertüzün, Ayşın.
dc.contributor.advisor Kuruoğlu, Ercan E.
dc.contributor.author Gençağa, Orhan Deniz.
dc.date.accessioned 2023-03-16T10:24:59Z
dc.date.available 2023-03-16T10:24:59Z
dc.date.issued 2007.
dc.identifier.other EE 2007 G46 PhD
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/13079
dc.description.abstract This thesis brings a unifying approach for modeling non-stationary non-Gaussian signals which are widely encountered in many multidisciplinary research fields. In the literature, different approaches have been used to model non-stationary signals. However, they could not fulfill the increasing needs where non-Gaussian processes are involved until the development of Sequential Monte Carlo techniques (particle filters). In general particle filtering, the problem is expressed in terms of nonlinear and/or non-Gaussian state-space equations and we need information about the functional form of the state variations. In this thesis, we bring a general solution for cases where these variations are unknown and the process distributions cannot be expressed by a closed form probability density function. We propose a novel modeling scheme which is as unified as possible to cover these problems. First, a novel technique is proposed to model Time-Varying Autoregressive Alpha Stable processes where unknown, time-varying autoregressive coefficients and distribution parameters can be estimated. Successful performances have been supported by posterior Cramer Rao Lower Bound values. Next, we extend our methodology to model cross-correlated signals where vector autoregressive processes with non-Gaussian driving signals can also be modeled. Later, this extension is used as a building block to provide a more unifying solution where both mixing matrix and latent processes are modeled from their mixtures. This can be interpreted as a solution for non-stationary Dependent Component Analysis. Successful simulation results verify that our methodology is very flexible and provides a unifying solution for the modeling of non-stationary processes in all cases described above.
dc.format.extent 30cm.
dc.publisher Thesis (Ph.D.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2007.
dc.relation Includes appendices.
dc.relation Includes appendices.
dc.subject.lcsh Gaussian processes.
dc.subject.lcsh Bayesian statistical decision theory.
dc.title Sequential Bayesian modeling of non-stationary non-Gaussian processes
dc.format.pages xxiii, 190 leaves;


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