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Iterative spectral identification of bone macroscopic properties described by a probability box
Author(s) -
Rosić Bojana,
Kumar Shivanand Sharana,
Vinh Hoang Truong,
G. Matthies Hermann
Publication year - 2018
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.201800404
Subject(s) - bayesian probability , identification (biology) , posterior probability , computer science , process (computing) , mathematics , algorithm , prior probability , statistical physics , artificial intelligence , physics , botany , biology , operating system
This paper considers Bayesian identification of macroscopic bone material characteristics given digital image correlation (DIC) data. As the evaluation of the full Bayesian posterior distribution is known to be computationally intense, here we consider the approximate estimation in a Newton‐like manner by using the theory of conditional expectation. The approach is extended to include the epistemic uncertainties in the process of modelling the prior.