🚀 go-pugleaf

I saw (but can't find) a report that someone has discovered a way
to hybridize Monte Carlo and Gaussian Numerical Integration: you
specify a tolerance e and a number of dimensions k, and the method
computes an integer N and a set of N points (and presumably weights)
in the unit k-dimensional hypercube such that in some probabilistic 
sense function values at those points allow the integral of the 
function to be estimated with precision better than e for "almost
all" functions, AND the behavior of N with k is much better than the 
e^-k that Monte Carlo gives.

Can anyone point me to this work?  Please email.

thanks,

-David West       dhw@iti.org