Rate at which a Gaussian random variable is the maximum in a set of independent Gaussian random variables Assume a random vector $X = [x_1, x_2, ..., x_n]$ where the random variables are independent and Gaussian distributed. At the same time, they are not identically distributed; they can have arbitrary means and variances. 
What is the probability that $x_k > x_i \forall i \neq k$? In other words, what is the probability that a realization of the random vector will yield $x_k$ as the maximum value in the vector? I'm looking for a closed form solution if one exists. 
Here's as far as I got addressing this problem. Assume $k=1$ without loss of generality.
$P\left(x_1 > x_i \forall_{i > 1}\right) = \int_{-\infty}^\infty p(x_1)P\left(x_1 > x_i \forall_{i>1}|x_1\right)dx_1$ 
$P\left(x_1 > x_i \forall_{i > 1}\right) = \int_{-\infty}^\infty p(x_1)\prod_{i=2}^n P\left(x_1 > x_i|x_1\right)dx_1$
$P\left(x_1 > x_i \forall_{i > 1}\right) = \int_{-\infty}^\infty p(x_1)\prod_{i=2}^nF_i(x_1)dx_1$
where $F_i(x)$ is the cumulative distribution function for $x_i$. 
I'm honestly not sure where to go from here. Any suggestions on whether there is a way forward or whether there is a better approach? Numerical integration is an option to move forward but I'd prefer a closed form solution if possible as it would open up other options for investigation in a larger problem I'm attacking.
Thanks!
UPDATE: The previous question-answer pair marked as providing an answer to this question is not for two reasons. (1) The question is seeking the probability that $x_k$ is the minimum, not the maximum and (2) no closed form solution is offered. If a closed form solution was derived, there might have been a similar approach in the answer that could have been leveraged here. But that is simply not present. 
UPDATE-2: There is now a closed form solution proposed to the related problem for the minimum that provides the basis for solving the question posed here. 
 A: $\newcommand{\N}{\mathcal N}\newcommand{\tr}{\mathrm{tr}}$Restating the question: let $X \sim \N(\mu, \Sigma)$, where $\Sigma = \mathrm{diag}(\sigma_1^2, \dots, \sigma_n^2))$. What is $\Pr(\forall i \ne k, X_k \ge X_i)$?
Now, this happens if and only if the $(n-1)$-dimensional random vector $Y$, where we drop the $k$th component and subtract each other dimension from $X_k$, is componentwise positive. Define the $(n-1) \times n$ matrix $A$ by taking the negative of $(n-1)$ identity and inserting a column of all $1$s as the $k$th column, so that $Y = A X$: if $k = n$, this is $A = \begin{bmatrix}-I & 1\end{bmatrix}$.
Then $Y \sim \N(A \mu, A \Sigma A^T)$. $A \mu$ is easy, and $A \Sigma A^T$ ends up dropping the $k$th row and column from $\Sigma$ and then adding $\sigma_k^2$ to each entry: $\Sigma' + \sigma_k^2 1 1^T$, where $\Sigma'$ drops $k$ from $\Sigma$.
Now, the probability in question is known naturally enough as a "Gaussian orthant probability". In general, these are difficult to get in closed form (here are a few; there are a bunch of algorithms out there to approximate them).
But we have a special form of covariance matrix here (a particularly simple rank-1 plus diagonal), which may yield a reasonable solution. Below is an effort towards that, but spoiler warning: I don't get to a closed form.

The probability in question is:
\begin{align}
\Pr\left( Y > 0 \right)
= \int_{y \in (0, \infty)^{n-1}} \left( 2 \pi \right)^{-\frac{n-1}{2}} \lvert A \Sigma A^T \rvert^{-\frac12} \exp\left( -\frac12 (y - A \mu)^T (A \Sigma A^T)^{-1} (y - A \mu) \right) \mathrm{d}y
.\end{align}
To avoid those pesky $A \mu$ terms, define $Z = Y - A \mu$, $\mathcal Z = \{ z : \forall i, z_i > (- A \mu)_i \}$. Then we care about
\begin{align}
\Pr\left( Z > - A \mu \right)
= \int_{z \in \mathcal{Z}} \left( 2 \pi \right)^{-\frac{n-1}{2}} \lvert A \Sigma A^T \rvert^{-\frac12} \exp\left( -\frac12 z^T (A \Sigma A^T)^{-1} z \right) \mathrm{d}z
.\end{align}
Applying the matrix determinant lemma:
\begin{align}
\lvert A \Sigma A^T \rvert
&= \left\lvert \Sigma' + \sigma_k^2 1 1^T \right\rvert
\\&= (1 + \sigma_k^2 1^T \Sigma^{-1} 1) \lvert \Sigma^{-1} \rvert
\\&= \left( 1 + \sum_{i \ne k} \frac{\sigma_k^2}{\sigma_i^2} \right) \prod_{i \ne k} \frac{1}{\sigma_i^2}
,\end{align}
so at least the normalization constant is easy enough.
To tackle the exponent, apply Sherman-Morrison:
\begin{align}
\left( A \Sigma A^T \right)^{-1}
&= \left( \Sigma' + \sigma_k^2 1 1^T \right)^{-1}
\\&= \Sigma'^{-1} - \frac{\sigma_k^2 \Sigma'^{-1} 1 1^T \Sigma'^{-1}}{1 + \sigma_k^2 1^T \Sigma'^{-1} 1}
\\&= \Sigma'^{-1} - \frac{1}{\frac{1}{\sigma_k^2} + \sum_{i \ne k} \frac{1}{\sigma_i^2}} \left[ \frac{1}{\sigma_i^2 \sigma_j^2} \right]_{ij}
\\&= \Sigma'^{-1} - \frac{1}{\tr(\Sigma^{-1})} \left[ \frac{1}{\sigma_i^2 \sigma_j^2} \right]_{ij}
\\
z^T (A \Sigma A^T)^{-1} z
&= \sum_i \frac{z_i^2}{\sigma_i^2}
- \frac{1}{\tr(\Sigma^{-1})}
  \sum_{ij} \frac{z_i z_j}{\sigma_i^2 \sigma_j^2}
\end{align}
and then the integral (after pulling out constants) is
\begin{align}
\int_{z \in \mathcal{Z}} &\exp\left( - \tfrac12 z^T (A \Sigma A^T)^{-1} z \right) \mathrm{d}z
\\&= \int_{z \in \mathcal{Z}} \prod_i \exp\left( - \frac{z_i^2}{2 \sigma_i^2} \right) \prod_{ij} \exp\left( \frac{1}{2 \tr(\Sigma^{-1})} \frac{z_i z_j}{\sigma_i^2 \sigma_j^2} \right) \mathrm{d}z
.\end{align}
This integral seems amenable to something smarter than just blind numerical integration, but it's late now....
A: I will use $P(X_k \geq X_i\;\;\forall i \neq k)$ to denote the probability that $X_k\geq X_i\;\;\forall i \neq k$
Let $X_1,...,X_n$ be an independent (but not identically) Gaussian distributed sample of random variables, i.e. $X_i \sim N(\mu_i,\sigma_i)$.  Also let $Z_{k,i}=X_k - X_i$.  
as @Dougal  pointed out, the random variables $Z_{k,1},...,Z_{k,n}$ (with the omission of $Z_{k,k}$) are NOT independent distributed.  Rather they follow a multivariate Gaussian where the 
$$
cov(Z_{k,i},Z_{k,j})=\begin{cases}
\sigma_k^2 \;\; \mathrm{for}\;\; i \neq j \\
\sigma_k^2+\sigma_i^2 \;\; \mathrm{for}\;\; i = j 
\end{cases}
$$
The marginal distributions are given by
$$Z_{k,i} \sim N\bigg(\mu_k-\mu_i,\sqrt{\sigma_k^2+\sigma_i^2}\bigg)\;\;\forall i \neq k$$
Thus, 
$$
P(X_k \geq X_i\;\;\forall i \neq k) = P(Z_{k,i} \geq 0\;\;\forall i \neq k)=$$ $$ \int_0^{\infty} \int_0^{\infty} . . . \int_0^{\infty} \Phi((Z_{k,1},...,Z_{k,n})_{-k}|\mu_k,\Sigma_k)\prod_{\stackrel{i=1}{i \neq k}}^ndz_k
$$ 
$\Phi$ being the multivariate Gaussian pdf, with $\mu_k$ and $\Sigma_k$ as the mean vector and covariance matrix formed from the above marginals and covariance.
The above multivariate distribution can be really tough to integrate over.  Most statistical software packages have functions for it though.  In R, for example, you may want to check out the  mnormt or mvnorm libraries.  However, I cannot speak to how accurate these methods are for very high dimensional problems. 
