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I am currently using different procedures to estimate the probability that a $D$-dimensional Gaussian random variable with mean $\mu$ and covariance $\Sigma$ lies within a sphere of radius $R$ that centered about the origin. That is, I am estimating $P(|| X ||_2 < R)$ where $X \sim N(\mu, \Sigma)$ and $X \in \mathbb{R}^D$.

I am wondering whether there is a way to obtain the exact value of this probability analytically (i.e. without using numerical integration or Monte Carlo)? I currently have two basic approaches to follow:

Approach 1

Find a way to analytically evaluate the integral:

$\int_{x \in S} (2\pi)^{-\frac{D}{2}}|\Sigma|^{-\frac{1}{2}} \exp(-\frac{1}{2} (x-\mu)^T \Sigma^{-1} (x-\mu) dx $

over the spherical region:

$S = \{||x|| < R \} = \{x^Tx < R^2\}$

Approach 2

Exploit the fact that given $X \sim N(\mu,\Sigma)$, the random variable

$(x-\mu)^T \Sigma^{-1} (x-\mu) \sim \chi^2(D) $

This implies that

$P( (x-\mu)^T \Sigma^{-1} (x-\mu) < R^2 ) = P(\chi^2(D) < R^2)$

which is very simple to calculate... though what I will actually need to evaluate is:

$P( x^T x < R^2) $

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It seems from the question that the sphere is center at the origin rather than the mean of the Gaussian distribution. Is that what you intended? – Michael Chernick May 16 '12 at 20:23
If you want to know whether or not the numerical integration for the intgral above gives you what you want, the answer is yes for a sphere centered at the origin. For the Monte carlo part I assume you know that you generate an x from N(μ,Σ) calculate its distribution from the origin and if that distance is less than R score 1 for the random variable W and otherwise 0. Repeat K times and use the sum of Wi/K has the estimate of the integral. – Michael Chernick May 16 '12 at 20:30
Yes, the sphere is at the origin. I included $\mu$ to keep the formulation general, but we can assume that $\mu = 0$. What I'm interested in is finding an exact answer without using a numerical integration procedure. – Elements May 16 '12 at 20:42
up vote 1 down vote accepted

There is not a closed form solution. So there is no better way to calculate it than numerical integration. The Monte Carlo method as I proposed it will approximate the integral and the approximation will be close to exact if you generate lots of samples like 100,000 or 1 million.

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