Bayesian calibration of a stochastic kinetic computer model using multiple data sources.

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

Biometrics

journal_title

Biometrics

authors

Henderson DA,Boys RJ,Wilkinson DJ

doi

10.1111/j.1541-0420.2009.01245.x

subject

Has Abstract

pub_date

2010-03-01 00:00:00

pages

249-56

issue

1

eissn

0006-341X

issn

1541-0420

pii

BIOM1245

journal_volume

66

pub_type

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