Oh, the Q function would then be the complement of the Gaussian Normal Distribution from Statistics. I had not heard a name for it before.
Q function = 1 2 π ∫ z ∞ e − x 2 2 d x \displaystyle \text{Q function} = \displaystyle \frac{1}{\sqrt{2\pi}}\int_{z}^{\infty}e^{\frac{-x^{2}}{2}}dx Q function = 2 π 1 ∫ z ∞ e 2 − x 2 d x
How exactly is your problem set up?. It appears to be:
∫ − ∞ ∞ e − x 2 1 2 π ∫ z ∞ e − x 2 2 d x d x \displaystyle \displaystyle \int_{-\infty}^{\infty}\frac{e^{-x^{2}}}{\frac{1}{\sqrt{2\pi}}\displaystyle \int_{z}^{\infty} e^{\frac{-x^{2}}{2}}dx}dx ∫ − ∞ ∞ 2 π 1 ∫ z ∞ e 2 − x 2 d x e − x 2 d x .
Looks rather ugly.
But, using the erf, the above would be:
− 2 π e r f ( z 2 ) − 1 \displaystyle \displaystyle \frac{-2\sqrt{\pi}}{erf\left(\frac{z}{\sqrt{2}}\right)-1} e r f ( 2 z ) − 1 − 2 π
Note that the famous Gauss integral is
∫ − ∞ ∞ e − x 2 d x = π \displaystyle \int_{-\infty}^{\infty}e^{-x^{2}}dx=\sqrt{\pi} ∫ − ∞ ∞ e − x 2 d x = π
Last edited: May 28, 2012