Publication:
Bayesian Parameter Estimation of Sinusoids with Simulated Annealing

Simple item page
dc.contributor.authorsUstundag, D.; Cevri, M.
dc.contributor.editorMastorakis, NE
dc.contributor.editorDemiralp, M
dc.contributor.editorMladenov, V
dc.contributor.editorBojkovic, Z
dc.date.accessioned2022-03-12T16:00:13Z
dc.date.accessioned2026-01-11T18:12:47Z
dc.date.available2022-03-12T16:00:13Z
dc.date.issued2008
dc.description.abstractIn this paper, we consider Bayesian analysis proposed by Bretthorst for estimating parameters from noisy data and combined it with a simulated annealing algorithm to obtain a global maximum of the posterior probability density of frequencies. Thus, this analysis offers different approach to finding parameter values through a directed, but random, search of the parameter space. For this purpose, we developed a Mathematica code of this Bayesian approach and used it for estimating parameters of sinusoids corrupted by random noise. The simulations results support the effectiveness of the method.
dc.identifier.doidoiWOS:000260494200017
dc.identifier.isbn978-960-6766-95-4
dc.identifier.issn1790-5109
dc.identifier.urihttps://hdl.handle.net/11424/224618
dc.identifier.wosWOS:000260494200017
dc.language.isoeng
dc.publisherWORLD SCIENTIFIC AND ENGINEERING ACAD AND SOC
dc.relation.ispartofISCGAV'08: PROCEEDINGS OF THE 8TH WSEAS INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, COMPUTATIONAL GEOMETRY AND ARTIFICIAL VISION
dc.relation.ispartofseriesRecent Advances in Computer Engineering
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectBayesian Statistical Inference
dc.subjectSimulated Annealing
dc.subjectParameter Estimations
dc.subjectOptimization
dc.subjectSpectral Analysis
dc.subjectALGORITHM
dc.titleBayesian Parameter Estimation of Sinusoids with Simulated Annealing
dc.typeconferenceObject
dspace.entity.typePublication
oaire.citation.endPage112
oaire.citation.startPage106
oaire.citation.titleISCGAV'08: PROCEEDINGS OF THE 8TH WSEAS INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, COMPUTATIONAL GEOMETRY AND ARTIFICIAL VISION

Files

Collections

Araştırma Çıktıları