ABSTRACT
A Bit of History.....................................................................................................243 For What Is It Good? ............................................................................................244 Simple Monte Carlo Example...............................................................................244 How Many Random Comparisons Are Enough?..................................................245 Output from Monte Carlo Analysis — The Frequency and Cumulative
Frequency Plots.................................................................................................246 Interpreting Cumulative Frequency Plots..............................................................247 Combining Monte Carlo Output Curves...............................................................253
Monte Carlo analysis uses the process of simulation to achieve a range of solutions to a problem. This technique generally is used to solve problems for which the definition of specific solution equations to calculate a specific answer is either too complex or too cumbersome to be practical. In Monte Carlo analysis, input variables are typically (but not necessarily) represented as distributions of values (see Chapter 15). Values making up these distributions represent the range and frequency of occurrence of the possible values for the variables. Also needed are equations that relate the input variables to the output (result or answer) variables. Monte Carlo analysis is a statistical procedure based upon random selection or chance. The name, of course, is taken from the city of Monte Carlo, Monaco, made famous by games of chance.
