Monte Carlo methods provide a way out of this exponential increase in computation time. As long as the function in question is reasonably well-behaved, it can be estimated by randomly selecting points in 100-dimensional space, and taking some kind of average of the function values at these points. By the central limit theorem, this method displays 1 / N convergence—i.e., quadrupling the number of sampled points halves the error, regardless of the number of dimensions.