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Remark: I posted this question in math stackexchange here and computer science stackexchange https://cs.stackexchange.com/ few weeks ago but obtain no answer.

Given a multi-dimensional gaussian function, defined by $$f(\boldsymbol{x})=\exp\left\{-\frac{1}{2} \boldsymbol{x}^T\boldsymbol{x} \right\}=\exp\left\{-\frac{1}{2} \sum_{i=1}^nx_i^2 \right\}$$ And an integration region as the form of a $n$-dimensional parallelepiped, defined by $$\mathcal{D} = \left\{\boldsymbol{l \le Lx\le u} \right\}$$ with

  • the lower triangular matrix $\boldsymbol{L}\in \Bbb R^{n\times n}$ where all lower elements and diagonal equal to $1$, all upper elements equal to $0$ $$ \boldsymbol{L} = \left( \begin{matrix} 1&0&0&\ldots&0\\ 1&1&0&\ldots&0\\ 1&1&1&\ldots&0\\ \vdots&\vdots&\vdots&\ddots&0\\ 1&1&1&\ldots&1\\ \end{matrix} \right) $$
  • the vectors $\boldsymbol{l,u} \in \Bbb R^n$: $\boldsymbol{l} = (l_1,...,l_n)'$ and $\boldsymbol{u} = (u_1,...,u_n)'$

The integration region

Are there any methods/algorithms that we can use to approximate the integral of $f(\boldsymbol{x})$ over $\mathcal{D}$ $$\int_{\mathcal{D}}f(\boldsymbol{x})d\boldsymbol{x}=\int_{\{\boldsymbol{l \le Lx\le u} \}}\exp\left\{-\frac{1}{2} \boldsymbol{x}^T\boldsymbol{x} \right\}d\boldsymbol{x}$$ satisfying

  • Fast computation (because later I must compute many integrals with different values of $\boldsymbol{l,u}$)
  • The accuracy doesn't need to be high (absolute error less than $10^{-3}$ is sufficient)

My attempt: we may use Monte Carlo simulation to approximate this integral but given the very specific form of the integration region and also the integrand, I hope there may exist a faster numerical method/algorithm/closed-form approximation.

Besides, we notices that the inversion matrix $\boldsymbol{L}^{-1}$ is an upper bi-diagonal matrix $$ \boldsymbol{L}^{-1} = \left( \begin{matrix} 1&-1&0&\ldots&0\\ 0&1&-1&\ldots&0\\ 0&\ddots&\ddots&\ddots&0\\ \vdots&\ddots&\ddots&\ddots&-1\\ 0&\ldots&\ddots&\ddots&1\\ \end{matrix} \right) $$ So, by making a change of variable $\boldsymbol{y = Lx}$, we can transform the integral into $$\int_{\mathcal{D}}f(\boldsymbol{x})d\boldsymbol{x}= \int_{\{\boldsymbol{l' \le y \le u'}\}} \exp \left\{-\frac{1}{2} \left(y_n^2+\sum_{i=1}^{n-1} (y_i-y_{i+1})^2 \right) \right\} d\boldsymbol{y}$$ with $\mathcal{D}' = \{\boldsymbol{l' \le y \le u'}\}$ is a rectangular region.

Thank you in advance!

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    $\begingroup$ Not good enough for an answer, so i'll just comment (if I can find some time i'll write up a better answer): 1) If you are okay with just approximations, you might consider estimating the resulting integral after your change of variables, using importance sampling. If n is small, it should work quite well, just be careful with "curse of dimensionality" and degeneracy of the associated likelihood function as n->\infty 2) I'm not convinced there isn't some "closed form" result including complimentary error functions, which most numerical packages have very robust implementations of. $\endgroup$
    – guest
    Commented Jun 28, 2021 at 3:56
  • $\begingroup$ @guest Thank you! I'm thinking of applying the Mill's ratio and approximate the integral by a lower and a upper bound. I hope these bounds approximate well the integral value. $\endgroup$
    – NN2
    Commented Jun 28, 2021 at 18:08
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    $\begingroup$ I don't know much about the Mill's ratio, i'll check it out. I think a possibility here is to multiply the top and bottom by the pdf of a Gaussian centered at the exact center of your region D'. The "likelihood function" is now your current (transformed) integrand over the pdf of the aforementioned Gaussian. From here, get samples from the newly centered Gaussian and apply rejection sampling monte carlo on the newly formed likelihood function. $\endgroup$
    – guest
    Commented Jun 29, 2021 at 4:04
  • $\begingroup$ @guest, I wouldn’t expect any helpful closed form; I don’t even see a closed form for $Pr[1<X<2, 0<Y<1]$ when $X$ and $Y$ are correlated normals. $\endgroup$
    – user44143
    Commented Feb 20, 2022 at 3:55

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