Iterative spectral identification of bone macroscopic properties described by a probability box

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.