Sentence examples for decision networks from inspiring English sources
Case studies illustrated draw from recent progress in manufacturing factories and processes, facility sensor networks, and supply decision networks.
This research investigates the applicability of Bayesian decision networks to study the impact of design decisions on the life cycle performance, including environmental friendliness, of a product.
Understanding the relationship between the structural organization of intracellular decision networks and the observable phenotypes they control is one of the exigent problems of modern systems biology.
In order to reduce the computational cost required for solving the POMDP problem for dialogue systems in which many emotions and dialogue acts might be considered, the authors employed decision networks to complement POMDP.
Computers are capable of making calculations at high and constant speed and of recalling large amounts of data and can, therefore, be used to manage decision networks of high complexity.
The National Cancer Institute has chosen texaphyrins as part of its NCI decision network--a group of what it considers to be the most promising up-and-coming therapies.
The methodology integrates dynamically and interactively risk and deterministic safety analysis and deliberation processes; the methodology has been integrated in a mobile multimedia group decision network system.
Wu (2010) developed an innovative quantitative approach to evaluate the performance of multi-level decision network structure by integrating cost DEA into the bi-level programming framework and create bi-level programming DEA model.
"We have not made a decision network on whether or not we are going to do those," Dunn said in response to recent reports that President Obama would be interviewed by Fox News' Major Garrett while he is in China.
Similarly, long time deadlines allow for very accurate responses and presumably entail low common inputs to the decision network.
The main prediction that follows from this work is the existence of two distinguishable decision behaviors depending on the mean input feeding the decision network.
