Sentence examples for model structure uncertainty from inspiring English sources

Model structure uncertainty relates to uncertainty about the causal structure of the modeled system: uncertainty within the boundaries chosen.

The HBMA is an ensemble method for prediction and is used to segregate the sources of model structure uncertainty in ANNs and investigate their variance contributions to total prediction variance.

We find that stochastic models, based upon conditional estimation and simulation techniques, identify some of the geological features (river deposit channels and low transmissivity regions associated to quartzite outcrops) and yield better fits to calibration data than the much simpler geology-based deterministic model, which cannot properly address model structure uncertainty.

We consider two different approaches to evaluate the model output uncertainty, the output error method that lumps all uncertainty into the observation noise term, and a method based on Stochastic Differential Equations (SDEs) that separates input and model structure uncertainty from observation uncertainty and allows updating of model states in real-time.

Epistemic uncertainty related to model structure uncertainty is partly addressed by oemof as well due to the multiple perspectives of the developers.

When data are available to allow for exercises like those described here, they can be used to assess model performance; descriptive analyses of the process taken to do so can contribute to a dialogue about different approaches that are taken by analysts to assess model process and model structure uncertainty.

Model structure uncertainties were analysed by a meta-analysis of similarities and differences of six energy models.

They can be particularly useful in expert elicitations about context or model structure uncertainties (see 'Typology of uncertainty') and are useful when the topic is multidisciplinary.

The simulation results show that compared with the traditional RTO methods, there is a significant improvement of production cost by enforcing the SCFO under model-parameter uncertainty, model-structure uncertainty and unknown process disturbance, which has laid an important foundation for the successful implementation of the plant-wide optimization and control for hydrometallurgy process.

This paper benchmarks a nonlinear Takagi Sugeno observer for wind speed estimation with enhanced Kalman Filter techniques: The performance and robustness towards model-structure uncertainties of the Takagi Sugeno observer, a Linear, Extended and Unscented Kalman Filter are assessed.

However, there are also some events where the RR uncertainty cannot explain the whole uncertainty observed in the simulated flow volumes indicating that there are additional sources of uncertainty that must be considered such as the uncertainty in the urban drainage model structure, the uncertainty in the urban drainage model calibrated parameters, and the uncertainty in the measured sewer flows.