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== Description == A methodology which investigates a model by generating random numbers and observing the changes in the output. It is usually applied when a problem cannot be solved analytically. == Software == There is a large number of packages available to perform such an analysis, including stand-alone packages and Excel add-ins, such as [http://www.palisade.com/ @RISK], [http://www.decisioneering.com/ Crystal Ball], [http://sensitivity-analysis.jrc.cec.eu.int/ SimLab] and many others. It can be easily performed from any statistical software package such as [http://www.mathworks.com/products/matlab/ Matlab] or [http://www.r-project.org/ R]. Many of these packages also allow for a more effective sampling design to for example distribute the random numbers evenly and avoid clusters. == Advantages == * '''Forces explicit acknowledgement of all sources of uncertainty''' All sources of uncertainty have to be stated. * '''Can take account of any distribution and correlation''' The method is not restricted to a specific set of distributions and can deal with all forms of correlation * '''Can include other sources of error''' The methodology can virtually include all sources of uncertainty * '''Can be applied to complex models''' model complexity is not a limiting factor as in so many other methodologies. == Disadvantagess == * '''Computationally intensive''' The methodology is computational intensive and become infeasible with models which have a long run time or too many sources of uncertainty. == References and Further reading == EPA, Risk assessment Forum, Guiding Principles for Monte Carlo Analysis, EPA/630/97/001, 1997. 1997. 158 Morgan, B.J., Elements of simulation. 1984: Chapman & Hall. 159 Ripley, B.D., Stochastic simulation. 1978: Wiley160
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