The goal herein is to understand where modeling errors and uncertainty originate from, and obtain model predictions that are statistically consistent with their respective measurements.
A combined strategy of model predictive control (MPC) and least squares online parameter estimation for the control of the hypnotic depth, measured by the Bispectral Index (BIS), under uncertainty is presented, where the uncertainty originates from patient variability.
The uncertainty originates from forecasting errors that are superimposed on simulated time series of load and wind generation, and this uncertainty is simulated as stochastic, normally distributed vectors of growing variance.
This uncertainty originates from the pivotal role of (and circular reasoning related to using) the corticosteroid response in deciding whether a patient does or does not have PMR.
This may reflect a perception that research is closer to being able to quantify uncertainty originating from these sources (e.g. Majda and Branicki 2012; Smith et al. 2009) than it is able to do so with uncertainty originating from other sources.
To estimate the uncertainties originated from the usage of single frequency in Eq. (9), we consider the half-width of the 0.035 0.056 Hz frequency range related to the center frequency (~25%).
In this study, two different calibration approaches, the multi-criteria and the generalized likelihood uncertainty estimation (GLUE) method, were used to quantify uncertainties originated from the use of multi-site discharge observations and the presence of equifinal solutions.
These uncertainties originated from the enzyme instability during sample storage and the assay.
Most uncertainties originate from difficulties in quantifying the impacts of disturbances and environmental variables.
These uncertainties originate from the incalculable small-scale environmental conditions and also from the climate models.
