Even if I accept licence checking as a good thing that is either an unacceptable behaviour or a bonelazy answer you got. Or both. Of course I have files which are created in future ... I do not use my PC for Maple alone.

It is not quite clear for me which constants you use and it may be you cut off to early (where the exp term is still contributing), but I would do it this way: Your integral writes as Int(erfc(b*sqrt(xi/(xi+1))) *exp(-a*xi)*a,xi=0..infinity) for b = B^2, a = 1/gamav. Here xi/(xi+1) approaches 1 from below for large xi and beyond xi = 1/epsilon-1 differs at most by epsilon from 1. Then split the integration at this point into Int(a*erfc(b*(xi/(xi+1))^(1/2))*exp(-a*xi),xi = 0 .. 1/epsilon-1) + Int(a*erfc(b)*exp(-a*xi),xi = 1/epsilon-1 .. infinity); The last is erfc(b)*exp(-a*(1/epsilon-1)) and the first should be done by working numerical. May be you refine that suggestion.

since nobody answers: you may add your hardware and operating system and may be graphical interface (and personally i just would reinstall instead of doing anything else ... and de-install first ... and if you do it try to avoid the blank in the directory name, overwrite it by Maple10 or similar within the installation dialog ... and if you have and old OS like WinME - do not use the standard interface)

sigh ...

would you mind to post that example? evalf(Int(f,t=0..1)) uses the NAG library i think ...

randomize():
B:=[stats[random,normald[0,1]](N+1)]:
W:=[stats[random,normald[0,1]](N+1)]:
Z:= zip((x,y) -> evalf( rho*x+sqrt(1-rho^2)*y ),B,W): # or evalhf?

Then Z and B will do, where rho is the desired correlation. In Maple 10 it is a bit different, but much (!) faster.

Once i translated Ooura's double exponential integration into Maple (find it uploaded, but i have neither cleaned up that port nor tested it carefully). That method is quite robust. So you can use Maple's new ability to generate a DLL and use that as a function directly in Maple. This might give you some of the speed improvements you are looking for. The additional advantage is: you can test values against Maple if you have some doubts. For distributions usually a Gauss-Legendre method also works well (using fixed numbers of sub-divisions), 16 point suffice. What i can not see is your chi^2, i thought it contains an exp in the integrand? Just a last thought: when i try to fit some pdf against data (to get parameters) i pass to the logarithm (and refine later if neccessary). If you want to fit against a cdf then i can imagine troubles (i am not sure whether this is a stable approach, especially if you are not absolutely convinced that your cdf is the correct one). PS: as i do not recognize how to attach files use that link http://www.axelvogt.de/axalom/maple/Ooura_DoubleExp_Integration.mws

the only thing i can imagine is that Maple methods can be worked out ... having not traced through the procs it is clear that they involve external callings for NAG routines what exactly are you looking for?