VEGAS algorithm
The VEGAS algorithm, due to G. Peter Lepage, is a method for reducing error in Monte [Carlo simulation]s by using a known or approximate probability distribution function to concentrate the search in those areas of the integrand that make the greatest contribution to the final integral.
The VEGAS algorithm is based on importance sampling. It samples points from the probability distribution described by the function so that the points are concentrated in the regions that make the largest contribution to the integral. The GNU Scientific Library provides a VEGAS routine.
Sampling method
In general, if the Monte Carlo integral of over a volume is sampled with points distributed according to a probability distribution described by the function we obtain an estimateThe variance of the new estimate is then
where is the variance of the original estimate,
If the probability distribution is chosen as then it can be shown that the variance vanishes, and the error in the estimate will be zero. In practice it is not possible to sample from the exact distribution g for an arbitrary function, so importance sampling algorithms aim to produce efficient approximations to the desired distribution.