The chapter describes the processes involved in reasoning through assessment arguments and by highlighting the foundations of model construction; the approach provides a framework that is grounded in the first principles of assessment and inference.
In this paper we propose a foundational modeling and ontology construction approach for a generic product that incorporates hardware and software components.
A modeling approach (i.e., using multiple model construction and model selection approaches) can be a powerful tool for identifying selection on specific functional sequence groups by comparing the frequency and distribution of polymorphisms.
We think that our modeling approach (i.e., using multiple model construction and model selection approaches) can be a powerful tool for identifying selection on specific functional sequence groups by comparing the frequency and distribution of polymorphisms.
The authors adopted an innovative, opportunistic approach to model construction, combining specific elements of traditional approaches and embedding them in a larger evidence synthesis exercise.
ECD offers a principled approach to assessment design and statistical model construction and serves a prescriptive approach for analysts to take advantage of this power in terms of assessment design, model estimation, and substantive inference.
Finally, the authors adopted an innovative, pragmatic approach to logic model construction that combined certain elements of traditional approaches (single analyst and collaborative group process) [ 17] that were adapted to the unique circumstances under which this work was to be carried out.
Therefore, a stepwise approach for model construction was followed.
The premise for this dynamic approach for model construction is the linkage between data lineage and positional uncertainty, i.e. the genesis and the process to collect and interpret data determines the positional accuracy and uncertainty, a widely accepted scenario in Subsurface Utility Engineering (SUE) (Sterling et al. 2009) and GIS.
A data-driven approach for model construction consists of inferring the model structure and model parameters from measurement data, which in the case of gene regulation most commonly are gene expression measurements obtained with microarray technology.
