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To consider this class of model uncertainties, a special technique of probability measure transformations is developed.
In this paper we examine the stability of this class of model for the cases of one to three contours.
This appears to be the first test of this class of model to flows without any homogeneous flow directions, which is typical of flows in complex geometries.
However, this class of model requires many physiological parameters that are difficult to measure for species not easily kept in captivity.
Despite these important advances, there are two criticisms about the use of the CPM's in DM: (i) Can this class of model reproduce severe basal glucose levels (e.g., larger than 300 mg/dl)? and (ii) Does a CPM reproduce a distinct glucose level as its parameters change or is it unique even if its parameters change?
As usual in this class of model, only the utility differences between the number of alternatives minus one can be identified.
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Based on the Markov Chain Monte Carlo (MCMC) simulation, this class of models provides a unified framework for modelling linear and non-linear effects simultaneously, as well as the underlying spatial process.
This is the first study of the parallelization of this class of models onto multicore processors.
These optimal designs aim at improving the estimation of parameters of this class of models.
Similarly, the concepts of residuals and posterior predictive distributions carry over to this class of models.
An essential concept of this class of models is introduced and their enhanced functionality is studied.
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