The authors discussed some directions for research and development of methods for assessing simulation variability, input uncertainty, and structural model uncertainty. Variance-based measures of importance for input and simulation variables arise naturally when using the quadratic loss function of the difference between the full model prediction y and the restricted prediction {tilde y}. The concluded that generic methods for assessing structural model uncertainty do not now exist. However, methods to analyze structural uncertainty for particular classes of models, like discrete event simulation models, may be attainable
The field of uncertainty quantification is evolving rapidly because of increasing emphasis on models...
Simulations not only facilitate new and unprecedented insights in highly sophisticated science areas...
Several uncertainty propagation algorithms are available in literature: (i) MonteCarlo simulations b...
The probability distribution of a model prediction is presented as a proper basis for evaluating the...
Predictive accuracy is the sum of two kinds of uncertainty–natural variability and modeling uncertai...
Uncertainty quantification is an emergent field in engineering mechanics that makes use of statistic...
The premise underlying this work is that introducing uncertainty considerations into simulation will...
The premise underlying this work is that introducing uncertainty considerations into simulation will...
The present paper addresses the question: ``What are the general classes of uncertainty and error so...
There is always a deviation between a model prediction and the reality that the model intends to rep...
The need for expressing uncertainty in stochastic simulation systems is widely recognized. However, ...
Uncertainty affects all aspects of building simulation: from the development of algorithms, through ...
A model is a simplified representation of the real world. Model uncertainty is a common issue in pre...
Defining and estimating uncertainty in simulations is essential in order to quantify the reliability...
Discrete-event stochastic simulation is a powerful tool for understanding and evaluating complex sys...
The field of uncertainty quantification is evolving rapidly because of increasing emphasis on models...
Simulations not only facilitate new and unprecedented insights in highly sophisticated science areas...
Several uncertainty propagation algorithms are available in literature: (i) MonteCarlo simulations b...
The probability distribution of a model prediction is presented as a proper basis for evaluating the...
Predictive accuracy is the sum of two kinds of uncertainty–natural variability and modeling uncertai...
Uncertainty quantification is an emergent field in engineering mechanics that makes use of statistic...
The premise underlying this work is that introducing uncertainty considerations into simulation will...
The premise underlying this work is that introducing uncertainty considerations into simulation will...
The present paper addresses the question: ``What are the general classes of uncertainty and error so...
There is always a deviation between a model prediction and the reality that the model intends to rep...
The need for expressing uncertainty in stochastic simulation systems is widely recognized. However, ...
Uncertainty affects all aspects of building simulation: from the development of algorithms, through ...
A model is a simplified representation of the real world. Model uncertainty is a common issue in pre...
Defining and estimating uncertainty in simulations is essential in order to quantify the reliability...
Discrete-event stochastic simulation is a powerful tool for understanding and evaluating complex sys...
The field of uncertainty quantification is evolving rapidly because of increasing emphasis on models...
Simulations not only facilitate new and unprecedented insights in highly sophisticated science areas...
Several uncertainty propagation algorithms are available in literature: (i) MonteCarlo simulations b...