Xref: utzoo sci.math.num-analysis:1987 sci.math.stat:2311 comp.theory:1966
Path: utzoo!utgpu!news-server.csri.toronto.edu!rpi!usc!sdd.hp.com!caen!ox.com!hela!dhw
From: dhw@iti.org (David H. West)
Newsgroups: sci.math.num-analysis,sci.math.stat,comp.theory
Subject: Probably-Good High-Dimensional Numerical Integration
Keywords: Monte Carlo Probabilistic Integration High Dimensionality
Message-ID: <1991May10.161247.25204@iti.org>
Date: 10 May 91 16:12:47 GMT
References: <1991May8.074247.1547@monu6.cc.monash.edu.au>
Organization: Industrial Technology Institute
Lines: 15

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