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The Creation of Puffin, the Automatic Uncertainty Compiler
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TLDR
An uncertainty compiler as mentioned in this paper is a tool that automatically translates original computer source code lacking explicit uncertainty analysis into code containing appropriate uncertainty representations and uncertainty propagation algorithms, which can apply intrusive uncertainty propagation methods to code or parts of codes and therefore more comprehensively and flexibly address both epistemic and aleatory uncertainties.Abstract:
An uncertainty compiler is a tool that automatically translates original computer source code lacking explicit uncertainty analysis into code containing appropriate uncertainty representations and uncertainty propagation algorithms. We have developed an prototype uncertainty compiler along with an associated object-oriented uncertainty language in the form of a stand-alone Python library. It handles the specifications of input uncertainties and inserts calls to intrusive uncertainty quantification algorithms in the library. The uncertainty compiler can apply intrusive uncertainty propagation methods to codes or parts of codes and therefore more comprehensively and flexibly address both epistemic and aleatory uncertainties.read more
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An Introduction to Copulas
TL;DR: This book discusses the fundamental properties of copulas and some of their primary applications, which include the study of dependence and measures of association, and the construction of families of bivariate distributions.
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The use of confidence or fiducial limits illustrated in the case of the binomial
C. J. Clopper,E. S. Pearson +1 more
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Interval Estimation for a Binomial Proportion
TL;DR: In this paper, the problem of interval estimation of a binomial proportion is revisited, and a number of natural alternatives are presented, each with its motivation and con- text, each interval is examined for its coverage probability and its length.
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Introduction to Interval Analysis
TL;DR: This unique book provides an introduction to a subject whose use has steadily increased over the past 40 years, and provides broad coverage of the subject as well as the historical perspective of one of the originators of modern interval analysis.
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Chapter 8 – Modelling Dependence with Copulas and Applications to Risk Management
TL;DR: One main aim of this paper is to show that when addressing the problem of simulating dependent data arises naturally in Monte Carlo approaches to risk management knowledge of copulas and copula based dependence concepts is important, and also the usefulness of copula ideas in this approach torisk management.