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Since we will be interested in predicting peak behaviour, the MDI is likely to be an important predictor.
For example, one may be interested in predicting levels of NO2 exposure at homes not measured in the pilot study.
Thus, in the example described above one might be interested in predicting the change in blood pressure in response to different doses of the drug.
Nevertheless, for real applications one might be interested in predicting the robustness of any grasp before actually executing it, i.e. the object should resist disturbances while being robust to uncertainties in perception and actuation, which can be tackled by using quality measures described in the previous sections.
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In prediction CFA, one is interested in predicting certain configurations by means of other configurations.
The fracture behavior of human bone is an interesting field of research for medical scientists, which are interested in predicting the fracture risk, but also for engineers, which are interested in mimicking bone structure in the design of new materials.
Given that we are interested in predicting levels of violence and disorder during public events, we take under consideration only the negative sentiment.
Therefore, steel suppliers who develop dual phase steels are interested in predicting the microstructure-properties relationship for optimization of microstructural design.
Predictor matrices arise in problems of science and engineering where one is interested in predicting future information from previous ones using linear models.
Given a network (G) where many links are observed, one is interested in predicting whether a link between nodes (a) and (b) exists (presumably the linkage between (a) and (b) has not been observed).
The choice of the particle state variables depends on the application; for example, chemical engineers concerned with the modeling of crystallizers are interested in predicting and controlling the size distribution of crystals in the product.
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