Variance importance of system components by Monte Carlo

Abstract

The authors present an algorithm to compute variance importance, a measure of uncertainty importance for system components. A simple equation has been derived for the measure, and Monte Carlo simulation is used to obtain numerical estimates. The algorithm overcomes NP-difficulty (non-polynomial difficulty) which exists in earlier methods for computing uncertainty importance, and is simpler, more accurate, and more practical. Moreover, it shows the direct relationship between probabilistic importance and uncertainty importance. An example illustrates the evaluation of Monte Carlo variance importance for a sample system.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>