@misc{10481/55333,
year = {2018},
month = {9},
url = {http://hdl.handle.net/10481/55333},
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.},
organization = {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.},
publisher = {MDPI},
keywords = {Inverse problem},
keywords = {Inference Bayesian updating},
keywords = {Model-class selection},
keywords = {Stochastic Inverse problem},
keywords = {Probability logic},
keywords = {Experimental design},
title = {Logical Inference Framework for Experimental Design of Mechanical Characterization Procedures},
author = {Rus Carlborg, Guillermo and Melchor, Juan},
}