Monte-Carlo based uncertainty analysis: Sampling efficiency and sampling convergence

By Hans Janssen. Reliability Engineering & System Safety, 2013

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Polynomial chaos expansions for uncertainty propagation and moment independent sensitivity analysis of seawater intrusion simulations (2015, methodology): “…the robustness of different UP methods, the normalized internal standard deviations of l and r have been estimated using the following equations (Janssen, 2013; Rajabi and Ataie-Ashtiani, 2014): stdðlÞ ¼ r ffiffiffiffiffi 1 nr s ð17-aÞ stdðrÞ ¼ r…”
Transitioning to Physics-of-Failure as a Reliability Driver in Power Electronics (2014, methodology): “In addition, Monte Carlo simulation is another important statistics tool to handle uncertainties to analyze the robustness margin, confidence level, and so on [69].”
A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis (2014, methodology): “As a result, any incorrect perception on the role of the different land-failure criteria can be easily conveyed from the expert0s opinion into the weight assignment (Kritikos and Davies, 2011; Feizizadeh and Blaschke, 2013a).”
Decision making under uncertainty in energy systems: state of the art (2013, methodology): “There are some methods for reducing the computational burden of MCS like Latin Hypercube Sampling (LHS) [9], sample-splitting approach [10] and fission and roulette method [11].”