Calibration of complex models through Bayesian evidence synthesis: a demonstration and tutorial.

ABSTRACT: Decision-analytic models must often be informed using data that are only indirectly related to the main model parameters. The authors outline how to implement a Bayesian synthesis of diverse sources of evidence to calibrate the parameters of a complex model. A graphical model is built to represent how observed data are generated from statistical models with unknown parameters and how those parameters are related to quantities of interest for decision making. This forms the basis of an algorithm to estimate a posterior probability distribution, which represents the updated state of evidence for all unknowns given all data and prior beliefs. This process calibrates the quantities of interest against data and, at the same time, propagates all parameter uncertainties to the results used for de

SUBMITTER: Jackson CH 

PROVIDER: S-EPMC4847637 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature