A Bayesian Approach to Morphological Models Characterization

Morphological models are commonly used to describe microstructures observed in heterogeneous materials. Usually, these depend upon a set of parameters that must be chosen carefully match experimental observations conducted on the microstructure. A common approach perform determination is try minimize an objective function, usually taken discrepancy between measurements computed simulations and observations, respectively. In this article, we present Bayesian for determining morphological models, based definition posterior distribution parameters. Monte Carlo Markov Chains (MCMC) algorithm then generate samples from identify optimal We show several examples allows us properly distinct potential correlations models.