Statistical Characterization of a Measurement – Approach using MCMC

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.
Type de document :
Communication dans un congrès
IMTC/2002, May 2002, Anchorage, United States. Proceedings of the IEEE 19th Instrumentation and Measurement Technology Conference, 2 (1373 - 1376), 〈10.1109/IMTC.2002.1007157 〉
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https://hal-supelec.archives-ouvertes.fr/hal-01104842
Contributeur : Alexandra Siebert <>
Soumis le : lundi 19 janvier 2015 - 12:12:17
Dernière modification le : jeudi 29 mars 2018 - 11:06:04

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Hana Baili, Gilles Fleury. Statistical Characterization of a Measurement – Approach using MCMC. IMTC/2002, May 2002, Anchorage, United States. Proceedings of the IEEE 19th Instrumentation and Measurement Technology Conference, 2 (1373 - 1376), 〈10.1109/IMTC.2002.1007157 〉. 〈hal-01104842〉

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