We have implemented such models in a nonlinear mixed effects modelling framework which allows us to relate pharmacokinetic response to outcome.
However, implementing such models at the system level may not always be practicable because of their complexity.
It is hoped that the lessons shared from the Beacon Project will help in developing and implementing such models.
We hope in the future to use our model for the discovery and prediction of underlying mechanisms of epidermal development but for now we focus on the technical aspects of implementing such models in GPU algorithms.
Several programs implementing such models have been developed, but they are limited in their possibilities: only a few particular models are available for likelihood optimization, and data sets cannot be easily generated using the resulting estimated parameters.
When implementing such model, the density p(x t |yt−1) is either estimated using the prior/likelihood function or using kernel density estimation [36,76].
In part, this has been motivated by the availability of geo-referenced survey data, and further, by the recent advances in software that can implement such complex models [17].
Two main routes may implement such internal models in the brain (see Fig. 8 and Miall 2003).
The key issues to implement such a model updating are discussed in this paper, such as design of experiment, parameter screening, construction of high-order polynomial response surface model, optimization methods and precision inspection of RS model.
