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I know this is a very simple one. If this is the formula for the two dimensional Gaussian (no covariance matrix considered - I have one mean and variance for each dimension):

$ A\exp{\left[ -\left(\displaystyle \frac{(\mu_{1} - x_{1})^2}{2\sigma_{1}^2} + \frac{(\mu_{2} - x_{2})^2}{2\sigma_{2}^2}\right) \right]}$

Would this be the one for the three dimensional and so on?

$ A\exp{\left[ -\left(\displaystyle \frac{(\mu_{1} - x_{1})^2}{2\sigma_{1}^2} + \frac{(\mu_{2} - x_{2})^2}{2\sigma_{2}^2} + \frac{(\mu_{3} - x_{3})^2}{2\sigma_{3}^2} + \dots \right) \right]}$

Thanks.

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    Yes, $A$ is a factor that ensures that the density integrates to $1$. Its value happens to be $A = \frac{1}{(2\pi)^{n/2}\sigma_1\sigma_2\cdots \sigma_n}.$ This site allows, in fact encourages, people writing the question to post their own answers and also accept their own answers, and I suggest that you do so based on the comments from Heike and myself.2012-02-23

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This would indeed be correct:

$ A\exp{\left[ -\left(\displaystyle \frac{(\mu_{1} - x_{1})^2}{2\sigma_{1}^2} + \frac{(\mu_{2} - x_{2})^2}{2\sigma_{2}^2} + \frac{(\mu_{3} - x_{3})^2}{2\sigma_{3}^2} + \dots \right) \right]}$

Please have a look at the comments if you want to ensure that things integrate to one.

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    ....provided the covariances are all zero.2012-02-23