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Model migration has been proved to be an effective modeling tool to adopt an existing base model from an old process to a similar, yet non-identical process.
Furthermore, an iterative approach that jointly accomplishes model migration and process optimization is presented.
In the model, migration stimulates health care provision at the lower end of the illness severity spectrum, improving health outcomes for those patients; sufferers of relatively severe medical conditions who can only be treated by doctors are made worse off.
The key idea is based on model migration, assuming that high-performance quality prediction models (defined as Base Models) have been available for the typical products.
We consider a Bayesian inference approach to enhance model migration, building on concepts laid out in an earlier paper (Lu and Gao, 2008a).
In addition, we provide a batch sequential design framework for iterative implementation of model migration, which thus avoids an exhaustive treatment of a predetermined number of design points.
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In order to apply the model one must inherently define the appropriate spatial resolution (i.e., the areas to model migrations between/within).
While injectivity can be understood using analytical models, migration and trapping calculations are generally carried out using numerical codes.
We did not use TreeMix to infer migration events because this software models migration between populations as occurring at a single instantaneous time point35, an assumption that is quite unrealistic for domestic species.
This work identifies a novel, cost-effective method for modeling migration patterns of and furthering conservation goals for a rare, low-density raptor species.
Chapter 1 examines the "broken" American and British print models, migration to online-only products, and whether the state should intervene to save papers with tax breaks, subsidies and stimulus packages.
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