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The importance of substitution model inadequacy in evolutionary studies has long been recognized [ 40- 44].
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We note that recent studies [ 38] have demonstrated that this topology (Paradoxopoda) can result from model inadequacies in phylogenetic reconstruction under ML (see discussion).
Many experiments in the physical and engineering sciences study complex processes in which bias due to model inadequacy dominates random error.
The model contains error terms for model inadequacy (model or process error) and for the sampling error in the estimated spatial means.
The Bayesian constant mean model (high model inadequacy) had marked prediction uncertainty which was reduced by accounting for the effects of environmental covariates in the Bayesian linear trend model (low model inadequacy), indicating that model inadequacy had a negative impact on prediction uncertainty.
In the Kennedy and O'Hagan framework for Bayesian calibration of physics models, selection of an appropriate prior form for the model discrepancy function is a challenging issue due to the lack of physics knowledge regarding model inadequacy.
The effects of some types of model inadequacy have been demonstrated on an automated system for experimental design to determine optimum conditions in a situation typical of process development.
However, the discrepancy may also reflect model inadequacy.
Residual diagnostics for non-constant variance and non-normality revealed no evidence of model inadequacy.
The basic concept of finite element model updating is that the measured and analytical modal data are unlikely to be equal due to the presence of noise in the measurements and model inadequacies (Lee and Eun 2009).
The proposed methodology accounts for uncertainties in the model parameters as well as physical model inadequacies, providing estimates of the rate constant that reflect both types of uncertainties.
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