EKSAKTA: Journal of Sciences and Data Analysis
VOLUME 11, ISSUE 1, February 2010

Segmentasi Bayesian Hirarki Untuk Model Ma Konstan Sepotong Demi Sepotong Berbasis Algoritma Reversible Jump Mcmc

Suparman Suparman (Unknown)

Article Info

Publish Date
01 Mar 2012

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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Journal Info
EKSAKTA: Journal of Sciences and Data Analysis
Website

Abbrev

eksakta

Publisher

Universitas Islam Indonesia

Subject

Chemical Engineering, Chemistry & Bioengineering Chemistry Earth & Planetary Sciences Materials Science & Nanotechnology

Description

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 ...