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<math>/mu = \varepsilon[x] = \int\limits_{-/infty}^{/infty} xp(x)\, dx</math>         
+
<math>\mu = \mathcal{E}[x] = \int\limits_{-/infty}^{/infty} xp(x)\, dx</math>         
 
</div>
 
</div>

Revision as of 17:00, 4 April 2013

Discriminant Functions For The Normal Density


       Lets begin with the continuous univariate normal or Gaussian density.

$ f_x = \frac{1}{\sqrt{2 \pi} \sigma} \exp \left [- \frac{1}{2} \left ( \frac{x - \mu}{\sigma} \right)^2 \right ] $


for which the expected value of x is

$ \mu = \mathcal{E}[x] = \int\limits_{-/infty}^{/infty} xp(x)\, dx $

Alumni Liaison

Ph.D. on Applied Mathematics in Aug 2007. Involved on applications of image super-resolution to electron microscopy

Francisco Blanco-Silva