Title :
Quality assurance for Monte Carlo risk assessment
Author_Institution :
Appl. Biomath., New York, NY, USA
Abstract :
Three major problems inhibit the routine use of Monte Carlo methods in risk and uncertainty analyses: correlations and dependencies are often ignored; input distributions are usually not available; and mathematical structure of the model is questionable. Most practitioners acknowledge the limitations induced by these problems, yet rarely employ sensitivity studies or other methods to assess their consequences. The paper reviews several computational methods that can be used to check a risk assessment for the presence of certain kinds of fundamental modeling mistakes, and to assess the possible error that could arise when variables are incorrectly assumed to be independent or when input distributions are incompletely specified
Keywords :
Monte Carlo methods; quality control; risk management; uncertainty handling; Monte Carlo risk assessment; computational methods; fundamental modeling mistakes; input distributions; mathematical structure; quality assurance; sensitivity studies; uncertainty analyses; Distributed computing; Information analysis; Mathematical model; Monte Carlo methods; Probability; Quality assurance; Risk analysis; Risk management; Tail; Uncertainty;
Conference_Titel :
Uncertainty Modeling and Analysis, 1995, and Annual Conference of the North American Fuzzy Information Processing Society. Proceedings of ISUMA - NAFIPS '95., Third International Symposium on
Conference_Location :
College Park, MD
Print_ISBN :
0-8186-7126-2
DOI :
10.1109/ISUMA.1995.527662