  Three sampling methods are compared for efficiency on a number of test
problems of various complexity for which analytic quadratures are available.
The methods compared are Monte Carlo with pseudo-random numbers, Latin
Hypercube Sampling, and Quasi Monte Carlo with sampling based on Sobol
sequences. Generally results show superior performance of the Quasi Monte Carlo
approach based on Sobol sequences in line with theoretical predictions. Latin
Hypercube Sampling can be more efficient than both Monte Carlo method and Quasi
Monte Carlo method but the latter inequality holds for a reduced set of
function typology and at small number of sampled points. In conclusion Quasi
Monte Carlo method would appear the safest bet when integrating functions of
unknown typology.
