Calibration of Complex Models through Bayesian Evidence Synthesis: A Tutorial
2015
This tutorial demonstrates how to implement a Bayesian synthesis of diverse sources of evidence to calibrate the parameters of a complex model. To illustrate these methods, the authors demonstrate how a previously developed Markov model for the progression of human papillomavirus (HPV-16) infection was rebuilt in a Bayesian framework. Transition probabilities between states of disease severity are inferred indirectly from cross-sectional observations of prevalence of HPV-16 and HPV-16–related disease by age, cervical cancer incidence, and other published information. The authors derive a Bayesian posterior distribution, in which scenarios are implicitly weighted according to how well they are supported by the data.
Calibration of Complex Models through Bayesian Evidence Synthesis: A Tutorial
Source:
Jackson C, Jit M, Sharples L et al. Calibration of Complex Models through Bayesian Evidence Synthesis: A Demonstration and Tutorial. Medical Decision Making 2015; 35 (2): 148-161. https://doi.org/10.1177/0272989X13493143