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EKSAKTA: Journal of Sciences and Data Analysis
Published by Universitas Islam Indonesia
ISSN : 27160459     EISSN : 27209326     DOI : 10.20885
Core Subject : Science, Engineering,
Chemical Engineering, Chemistry & Bioengineering Chemistry Earth & Planetary Sciences Materials Science & Nanotechnology
Ekstakta is an interdisciplinary journal with the scope of mathematics and natural sciences that is published by Fakultas MIPA Universitas Islam Indonesia. All submitted papers should describe original, innovatory research, and modelling research indicating their basic idea for potential applications. The Journal particularly welcomes submissions that focus on the progress in the field of mathematics, statistics, chemistry, physics, biology and pharmaceutical sciences.
Arjuna Subject : Kimia(semua kategori) - Kimia (Lain-Lain)
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Search results for , issue "VOLUME 11, ISSUE 1, February 2010" : 1 Documents clear
Segmentasi Bayesian Hirarki Untuk Model Ma Konstan Sepotong Demi Sepotong Berbasis Algoritma Reversible Jump Mcmc Suparman, Suparman
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 11, ISSUE 1, February 2010
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

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Abstract

This paper addresses the problem of the signal segmentation within a hierarchical Bayesian framework by using reversible jump MCMC sampling. The signal is modelled by piecewise constant MA processes where the numbers of segments, the position of abrupt, the order and the coefficients of  the MA processes for each segment are unknown. The reversible jump MCMC algorithm is then used to generate samples distributed according to the joint posterior distribution of the unknown parameters. These samples allow to compute some interesting features of the a posterior distribution. Main advantage of the algorithm reversible jump MCMC algorithm is produce the joint estimators for the parameter and hyper parameter in hierarchical Bayesian.  The performance of the this methodology is illustrated via several simulation results.   Keywords :     Hierarchical Bayesian model, Reversible Jump MCMC methods, Signal  Segmentation, piecewise constant Moving-Average (MA) processes

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