Logical Inference Framework for Experimental Design of Mechanical Characterization Procedures
Metadata
Show full item recordEditorial
MDPI
Materia
Inverse problem Inference Bayesian updating Model-class selection Stochastic Inverse problem Probability logic Experimental design
Date
2018-09-07Referencia bibliográfica
Logical Inference Framework for Experimental Design of Mechanical Characterization Procedures. Sensors 2018, 18, 2984.
Sponsorship
This research was supported by the Ministry of Education DPI2014-51870-R, DPI2017-85359-R and UNGR15-CE-3664, Ministry of Health DTS15/00093 and PI16/00339, and Junta de Andalucía PIN-0030-2017 and PI-0107-2017 projects, and university of Granada PP2017-PIP2019.Abstract
Optimizing an experimental design is a complex task when a model is required for indirect
reconstruction of physical parameters from the sensor readings. In this work, a formulation is
proposed to unify the probabilistic reconstruction of mechanical parameters and an optimization
problem. An information-theoretic framework combined with a new metric of information density
is formulated providing several comparative advantages: (i) a straightforward way to extend
the formulation to incorporate additional concurrent models, as well as new unknowns such as
experimental design parameters in a probabilistic way; (ii) the model causality required by Bayes’
theorem is overridden, allowing generalization of contingent models; and (iii) a simpler formulation
that avoids the characteristic complex denominator of Bayes’ theorem when reconstructing model
parameters. The first step allows the solving of multiple-model reconstructions. Further extensions
could be easily extracted, such as robust model reconstruction, or adding alternative dimensions to
the problem to accommodate future needs.