Simulation and the Monte Carlo Method Dirk P. Kroese, Reuven Y. Rubinstein
Publisher: Wiley-Interscience

The method that I used to do the calculation for this model was Monte Carlo simulation. Answer: A Monte Carlo simulation might sound like a ride you'd find at Disneyworld, but it's actually a statistical method used to determine probability and assess risk. Yet these simulations of paleo “spikes” involve introducing raw-data spikes and determining whether the processing will eliminate the spikes. The Monte Carlo method would then inflate this to a respectable looking sample of 1000 data points. The default random number generator for R uses the Mersenne Twister algorithm which is of very high quality, has a huge period and is well suited for Monte Carlo simulations. Completing my extension of geiger into object oriented design whereby I can easily apply the Monte Carlo approach to test fits using function calls to model fits such as update() and simulate(). But what happens to this assumption when you start to use a Monte Carlo method to bulk up your sample? The Monte Carlo method refers to several ways of using randomly generated numbers fed into a computer simulation many times to estimate the likelihood of an event, specialists in the field say. To give an extreme example, suppose that only one proxy measurement was input into the procedure. This was purely a calculation technique -- MC is the easiest way to do complex calculations when you are working with uncertain inputs.