Risk assessment is an important and popular aid in the decision making process. The aim of risk assessment is to estimate the severity and likelihood of harm to human health from exposure to a substance or activity that under plausible circumstances can cause to human health. In risk assessment, it is most important to know the nature of all available information, data or model parameters. More...

Geostatistics is a branch of statistics dealing with spatial phenomena modelled by random functions. In particular, it is assumed that, under some wellchosen simplifying hypotheses of stationarity, this probabilistic model, i.e. the random function describing spatial dependencies, can be completely assessed from the dataset by the experts. Kriging is a method for estimating or predicting the sp...

This report documents the results of an FY09 ASC V&V Methods level 2 milestone demonstrating new algorithmic capabilities for mixed aleatory-epistemic uncertainty quantification. Through the combination of stochastic expansions for computing aleatory statistics and interval optimization for computing epistemic bounds, mixed uncertainty analysis studies are shown to be more accurate and efficien...

For structural systems with both epistemic and aleatory uncertainties, the effect of epistemic uncertainty on failure probability is measured by the variance based sensitivity analysis, which generally needs a ‘‘triple-loop’’ crude sampling procedure to solve and is time consuming. Thus, the Kriging method is employed to avoid the complex sampling procedure and improve the computational efficie...

In this paper, we investigate the use of event models for automated planning. Event models are the action defining structures used to define a semantics for dynamic epistemic logic. Using event models, two issues in planning can be addressed: Partial observability of the environment and knowledge. In planning, partial observability gives rise to an uncertainty about the world. For single-agent ...

Reliability analysis and trade-offs between safety cost with insufficient data represent an inevitable problem during the early stage of structural design. In this paper, efficient uncertainty theory-based reliability a design method are proposed under epistemic uncertainty. The factors influencing structure regarded as uncertain variables. Based on this, new metric termed measure is employed t...

Managing uncertainty is an integral part of making well-informed engineering decisions. When formulating a design problem, many of the variables and models contain epistemic uncertainty, uncertainty due to lack of knowledge. If this lack of knowledge is significant, it may be advantageous to acquire additional information before making a design decision. In this paper, we develop a framework fo...

Objective To review the research literature on epistemic cognition in medical education. Methods We conducted database searches using keywords related to epistemic cognition and medical education or practice. In duplicate, authors selected and reviewed empirical studies with a central focus on epistemic cognition and participant samples including medical students or physicians. Independent th...

Sampling-based methods for uncertainty and sensitivity analysis are reviewed. The following topics are considered: (i) Definition of probability distributions to characterize epistemic (Type B) uncertainty in analysis inputs, (ii) Generation of samples from uncertain analysis inputs, (iii) Propagation of sampled inputs through an analysis, (iv) Presentation of uncertainty analysis results, and ...