qscmvnv.m

   [ P E ] = QSCMVNV( M, R, A, CN, B )
     uses a randomized quasi-random rule with m points to estimate an
     MVN probability for positive semi-definite covariance matrix r,
     with constraints a < cn*x < b. If r is nxn and cn is kxn, then
     a and b must be column k-vectors.
    Probability p is output with error estimate e.
     Example use:
      r = [ 4 3 2 1; 3 5 -1 1; 2 -1 4 2; 1 1 2 5 ];
      a = [ -inf 1 -5 ]'; b = [ 3 inf 4 ]';
      cn = [ 1 2 3 -2; 2 4 1 2; -2 3 4 1 ];
      [ p e ] = qscmvnv( 5000, r, a, cn, b ); disp([ p e ])
 
   This function uses an algorithm given in the paper by Alan Genz:
    "Numerical Computation of Multivariate Normal Probabilities", in
      J. of Computational and Graphical Stat., 1(1992), 141-149.
   The primary references for the numerical integration are 
    "On a Number-Theoretical Integration Method"
      H. Niederreiter, Aequationes Mathematicae, 8(1972), 304-11, and
    "Randomization of Number Theoretic Methods for Multiple Integration"
      R. Cranley and T.N.L. Patterson, SIAM J Numer Anal, 13(1976), 904-14.
 
    Alan Genz is the author of this function and following Matlab functions.
           Alan Genz, WSU Math, PO Box 643113, Pullman, WA 99164-3113
           Email : AlanGenz@wsu.edu
 
 
  Copyright (C) 2014, Alan Genz,  All rights reserved.               
 
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