Signal segmentation using maximum a posteriori probability estimator

Author

Popescu, Theodor D.

Author_Institution

Nat. Inst. for R&D in Inf., Bucharest, Romania

fYear

2013

fDate

23-25 Oct. 2013

Firstpage

1

Lastpage

5

Abstract

The objective of the paper is to present a segmentation method, using maximum a posteriori probability (MAP) estimator, with application in decision making, based on change detection and diagnosis. Some experimental results obtained by Monte-Carlo simulations for signal segmentation using different signal models, including models with changes in the mean, in FIR, AR and ARX model parameters, that make the object of investigation in other papers, are presented to prove the effectiveness of the approach.

Keywords

FIR filters; Monte Carlo methods; autoregressive processes; maximum likelihood estimation; regression analysis; signal detection; AR model parameters; ARX model parameters; FIR model parameters; MAP estimator; Monte Carlo simulation; autoregressive model with exogenous variable model; change detection; decision making; finite impulse response model; maximum a posteriori probability estimator; signal segmentation method; Biological system modeling; Finite impulse response filters; Maximum a posteriori estimation; Monte Carlo methods; Noise; Random sequences; Vectors; Change detection; Monte-Carlo simulation; decision making; diagnosis; regression models;

fLanguage

English

Publisher

ieee

Conference_Titel

Application of Information and Communication Technologies (AICT), 2013 7th International Conference on

Conference_Location

Baku

Print_ISBN

978-1-4673-6419-5

Type

conf

DOI

10.1109/ICAICT.2013.6722734

Filename

6722734