Probabilistic forecasting models describe the aleatory variability of natural systems as well as our epistemic uncertainty about how the systems work. Testing a model against observations exposes ontological errors in the representation of a system and its uncertainties. We clarify several conceptual issues regarding Separation of Aleatory and Epistemic Uncertainty in Probabilistic Model Validation ... uncertainty sources, model validation metrics that compare the distributions of model prediction and ... Propagation of PDF’s through a model Quantification of confidence due to limited data/information . Regardless of what type of uncertainty the PDF represents, objective is to quantify the output uncertainty that results from the input uncertainty • Specific to epistemic uncertainty • Typically involves statistical analysis the aleatory uncertainty and therefore cannot significantly improve the precision of the prediction. Furthermore, when apply-ing probabilistic model validation methods, the primary interest is the epistemic uncertainty (i.e., parameter uncertainty and model form uncertainty) in the prediction, and the separation of uncer- On the other hand, information about epistemic uncertainty directly supports decisions about data collection and model improvement. Therefore, the focus of this paper is the impact of epistemic uncertainty on model validation and how to separate the contributions of aleatory and epistemic uncertainty when the available data permits. Differentiating between aleatory and epistemic uncertainty in risk assessments is important because it can lead to different results when propagating uncertainty in a model (e.g., Nauta 2000), and more importantly, it allows the uncertainty about an estimated risk to be characterized. Epistemic uncertainty quantification methods. In literature, the most common methods of modeling epistemic uncertainty are the following. Bayesian probability is a method that appoints a frequency or probability of an event, based on an educated guess or a personal belief.; Evidence theory, also known and as Dempster-Shafer theory or theory of belief functions. Since the epistemic uncertainty in the model inputs and the validation data have already been converted into probabilistic information (in 2 Probabilistic representation of epistemic model inputs, 3 Bayesian updating using epistemic validation data), the Bayes factor-based model validation approach proposed by Rebba et al. is now also ... Model validation methods have been widely used in engineering design to evaluate the accuracy and reliability of simulation models with uncertain inputs. Most of the existing validation methods for aleatory and epistemic uncertainty are based on the Bayesian theorem, which needs a vast number of data to update the posterior distribution of the model parameter.
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