Hierarchical Models for Uncertainty Quantification: An Overview
Analyses of complex processes should account for the uncertainty in the data, the processes that generated the data, and the models that are used to represent the processes and data. Accounting for these uncertainties can be daunting in traditional statistical analyses. In recent years, hierarchical statistical models have provided a coherent probabilistic framework that can accommodate these multiple sources of quantifiable uncertainty. This overview describes C.K. Wikle Department of Statistics, University of Missouri, Columbia, MO, USA e mail: wiklec@missouri.edu (c) Springer International Publishing Switzerland 2015 R. Ghanem et al. (eds. Handbook of Uncertainty Quantification, DOI 10.1007/978 3 319 11259 6_4 1 1