Abstract:
: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.
journal_name
Biometricsjournal_title
Biometricsauthors
Henderson DA,Boys RJ,Wilkinson DJdoi
10.1111/j.1541-0420.2009.01245.xsubject
Has Abstractpub_date
2010-03-01 00:00:00pages
249-56issue
1eissn
0006-341Xissn
1541-0420pii
BIOM1245journal_volume
66pub_type
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