Journal
BIOMETRICS
Volume 66, Issue 1, Pages 249-256Publisher
WILEY
DOI: 10.1111/j.1541-0420.2009.01245.x
Keywords
Bayesian modeling; Markov chain Monte Carlo; Mitochondrial DNA; Random effects; Systems biology
Funding
- BBSRC [BBS/B/16550]
- Biotechnology and Biological Sciences Research Council [BBS/B/16550] Funding Source: researchfish
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In this article, we describe a Bayesian approach to the calibration of a stochastic computer model of chemical kinetics. As with many applications in the biological sciences, the data available to calibrate the model come from different sources. Furthermore, these data appear to provide somewhat conflicting information about the model parameters. We describe a modeling framework that allows us to synthesize this conflicting information and arrive at a consensus inference. In particular, we show how random effects can be incorporated into the model to account for between-individual heterogeneity that may be the source of the apparent conflict.
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