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

See this Gist for the improved code. But Welton Investment Corporation has a great little paper out showing just how unhelpful Monte Carlo simulations can be. Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference . Yet these simulations of paleo “spikes” involve introducing raw-data spikes and determining whether the processing will eliminate the spikes. To give an extreme example, suppose that only one proxy measurement was input into the procedure. The location and distance information is used in association with a Monte Carlo simulation and Bayesian decision model to identify pre-crash condition. Monte Carlo simulation is one of the best tools for performing realistic analysis of complex systems as it allows most of the limiting assumptions on system behavior to be relaxed. But what happens to this assumption when you start to use a Monte Carlo method to bulk up your sample? Under the assumed model, the cumulative-sum processes converge weakly to zero-mean Gaussian processes whose distributions can be approximated through Monte Carlo simulation. EDIT: I've updated this code to work with distributions requiring more than two parameters. To evaluate the applicability of both approaches, a quantitative comparison of both methods under typically encountered experimental conditions is necessary. The Monte Carlo method would then inflate this to a respectable looking sample of 1000 data points. Markov chain Monte Carlo - Wikipedia, the free encyclopedia Markov chain Monte Carlo methods that change dimensionality have also long.