Eksakta: Jurnal Ilmu-Ilmu MIPA (2012-03-01)

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

  • Suparman Suparman

Journal volume & issue
Vol. 11, no. 1

Abstract

Read online

/* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} 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