Title :
A Novel Approach for Adaptive Bayesian Prior Selection in Ill-conditioned Measurement Problems
Author :
D´Antona, Gabriele ; Rocca, Luca
Author_Institution :
Politecnico di Milano, Milano
Abstract :
A novel approach for ill-conditioned inverse problems´ solution is presented. In the novel approach the prior knowledge about the quantity to be estimated, necessary to combat the ill-conditioning of the problem, is not a priori assigned; it is instead adaptively determined on the basis of the available measurement data. The suggested estimator is particularly suited in case the measurement process is performed in a very limited context of knowledge about the measurand, and, as a consequence, the selection of proper prior knowledge may become a difficult task.
Keywords :
Bayes methods; adaptive estimation; inverse problems; measurement theory; adaptive Bayesian selection; ill conditioned measurement problems; measurement process; prior knowledge; Bayesian methods; Bioelectric phenomena; Electric potential; Inverse problems; Measurement uncertainty; Noise measurement; Noise reduction; Particle measurements; Performance evaluation; Vectors; Bayesian analysis; Inverse Problems; regularization;
Conference_Titel :
Instrumentation and Measurement Technology Conference Proceedings, 2007. IMTC 2007. IEEE
Conference_Location :
Warsaw
Print_ISBN :
1-4244-0588-2
DOI :
10.1109/IMTC.2007.379238