Statistical characterization of a measurement approach using MCMC

Author

Baili, H. ; Fleury, G.

Author_Institution

Ecole Superieure d´´Electricite, Gif-sur-Yvette, France

Volume

2

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

1373

Abstract

A measurement is any quantity to be observed within a system; we talk about indirect measurement when this quantity cannot be directly given by some sensors. This paper proposes a probabilistic approach to characterize a dynamic continuous measurement by a knowledge-based uncertain model, using a Monte-Carlo technique with Markov chains (MCMC). The method is far simpler than the Monte-Carlo´s one or the numerical resolution of the Fokker-Planck equation; looking at the precision, it is also quite satisfactory.

Keywords

Markov processes; Monte Carlo methods; characteristics measurement; knowledge based systems; statistical analysis; uncertain systems; Fokker-Planck equation numerical resolution; MCMC precision; Markov chains; Monte-Carlo techniques; dynamic continuous measurement probabilistic characterization; indirect measurement; knowledge-based uncertain models; measurement statistical characterization; stochastic calculus; uncertainty; Calculus; Density measurement; Differential equations; Mathematical model; Measurement uncertainty; Physics; Random processes; Sensor systems; Stochastic processes; Time measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Instrumentation and Measurement Technology Conference, 2002. IMTC/2002. Proceedings of the 19th IEEE

ISSN

1091-5281

Print_ISBN

0-7803-7218-2

Type

conf

DOI

10.1109/IMTC.2002.1007157

Filename

1007157