Bayesian inference for the stochastic identification of elastoplastic   material parameters: Introduction, misconceptions and insights
  We discuss Bayesian inference (BI) for the probabilistic identification of material parameters. This contribution aims to shed light on the use of BI for the identification of elastoplastic material parameters. For this purpose a single spring is considered, for which the stress-strain curves are artificially created. Besides offering a didactic introduction to BI, this paper proposes an approach to incorporate statistical errors both in the measured stresses, and in the measured strains. It is assumed that the uncertainty is only due to measurement errors and the material is homogeneous. Furthermore, a number of possible misconceptions on BI are highlighted based on the purely elastic case. 
Hussein Rappel, Lars A.A. Beex, Jack S. Hale, Stephane P.A. Bordas
cs.CE