The calibration procedure adopted here is based on the Bayes' theorem: P posterior (X|y) = P likelihood (y|X)P prior (X) P evidence (y) (36) where P prior (X) is the probability distribution (or probability density function) of input parameters given a priori, the conditional probability P likelihood (y|X) represents the likelihood of observations y occur subject to input parameters data X, and P evidence (y) denotes the evidence from observed data y. The conditional probability P posterior (X|y) is called posterior distribution of input parameters X given that y is true. The application of equation 36 combined with Markov Chain Monte Carlo (MCMC) methods results in a procedure that fits the posterior distribution in order to obtain the best parameters' distributions X given the observed data y.