Uncertainty analysis assesses the confidence in modelling results (including parameter estimates and model outputs/predictions) by quantification of the propagation of various sources of errors in the model input and design. The errors may originate from two sources: one PH797804 the quality and amount of data used to develop the model (each measurement is associated with a measurement noise and the system is usually observed partially); another is the model structure, which may not be a perfect representation of the real system. As shown in Eqs. (36)–(38), parameter uncertainty in terms of confidence intervals and correlation matrix can be estimated from the calculation of the Fischer information matrix based on local sensitivity analysis. Additionally, model prediction uncertainty will be investigated in Section 4.2 by two related methods: quasi Monte-Carlo (QMC) simulation using Sobol\' sequences and global sensitivity analysis using the Sobol’ method (GSA). Fig. 2 depicts the framework used in pelvic girdle study.