Here we address the experiment design problem from a "dual" point of view and in a closed-loop setting: given a maximum allowable control-oriented model uncertainty measure compatible with our robust control specifications, what is the cheapest identification experiment that will give us an uncertainty set that is within the required bounds?
Compared to the four-term model presented in the previous paper, the improved model fit the data better, had excellent cross-validation performance, and the predicted Zave of a validation point was within model uncertainty of the measured value.
This paper proposes using hierarchical Bayes multinomial probit models to construct respondent uncertainty measures based on the utility difference and a concept called utility suppression, which is quantified by the derivative of the inverse Mills ratio.
A spatially representative 10% holdout dataset was used for model validation and measures of model uncertainty included the mean prediction error (MPE) and the mean absolute prediction error (MAPE).
It is worth mentioning that Bion-Nadal (2004) studies dynamic monetary risk measures in a continuous time setting and their time consistency property in the context of model uncertainty when the class of probability measures is not specified.
By utilizing high-fidelity data and uncertainty measures, mathematical models for tensors are created.
Few direct comparisons of the models exist and therefore, comparing the models like-for-like provides a representative measure of model uncertainty in forecasting potential impact and allows us to identify areas of impact for which we have the greatest confidence (Fig. 8).
Inspection of the plots showing measures of the model uncertainty (2 σ) versus the mean probability, for each slope unit, reveals that LDA and LR models are characterized by a smaller variability than the QDA model.
We model uncertainty with non-probabilistic convex models, and measure the amount of uncertainty with the expansion parameters of the convex models.
Another way of saying that a matrix is not full rank is that it is singular, and due to numerical inaccuracy of digital computers and model uncertainty, condition number is used to measure how close to singularity a matrix is.
