# cdfMvn2e¶

## Purpose¶

Computes the multivariate normal cumulative distribution function with error management over the range $$[a,b]$$.

## Format¶

{ y, err, retcode } = cdfMvn2e(ctl, l_lim, u_lim, corr, nonc)
Parameters
• ctl (struct) –

instance of a cdfmControl structure with members

 ctl.maxEvaluations scalar, maximum number of evaluations. ctl.absErrorTolerance scalar, absolute error tolerance. ctl.relErrorTolerance scalar, error tolerance.

• l_lim (NxK matrix) – lower limits.

• u_lim (NxK matrix) – upper limits.

• corr (KxK matrix) – correlation matrix.

• nonc (Kx1 vector) – non-centrality vector.

Returns
• p (Nx1 vector) – Each element in p is the cumulative distribution function of the multivariate normal distribution for each corresponding columns in x. p will have as many elements as the inputs, u_lim and l_lim, have rows. $$Pr(X ≥ l\_lim \text{ and } X ≤ u\_lim|corr, nonc)$$.

• err (Nx1 vector) – estimates of absolute error.

• retcode (Nx1 vector) –

return codes.

 0 normal completion with $$err < ctl.absErrorTolerance$$. 1 $$err > ctl.absErrorTolerance$$ and ctl.maxEvaluations exceeded; increase ctl.maxEvaluations to decrease error. 2 $$K > 100$$ or $$K < 1$$. 3 corr not positive semi-definite. missing corr not properly defined.

## Examples¶

### Uncorrelated variables¶

// Lower limits of integration for K dimensional multivariate distribution
a = {-1e4 -1e4};

// Upper limits of integration for K dimensional multivariate distribution
b = {0 0};

/*
** Identity matrix, indicates
** zero correlation between variables
*/
corr = { 1 0,
0 1 };

// Define non-centrality vector
nonc  = {0, 0};

// Define control structure
struct cdfmControl ctl;
ctl = cdfmControlCreate();

/*
** Calculate cumulative probability of
** both variables being from -1e4 to  0
*/
{ p, err, retcode } = cdfMvn2e(ctl, a, b, corr, nonc);


After the above code, both p equal to 0.25.

$\Phi = P(-10000 \leq X_1 \leq 0 \text{ and } -10000 \leq X_2 \leq 0) \approx 0.25.$

### Compute the multivariate normal cdf at 3 separate pairs of upper limits¶

/*
** Limits of integration
** -5 ≤ x1 ≤ -1 and -8 ≤ x2 ≤ -1.1
** -10 ≤ x1 ≤ 0 and -10 ≤ x2 ≤ 0.1
** 0 ≤ x1 ≤ 1 and 0 ≤ x2 ≤ 1.1
*/
a = {  -5  -8,
-20 -10,
0   0 };

b = {  -1 -1.1,
0  0.1,
1  1.1 };

// Correlation matrix
corr = {   1 0.31,
0.31    1};

// Define non-centrality vector
nonc  = {0, 0};

// Define control structure
struct cdfmControl ctl;
ctl = cdfmControlCreate();

/*
** Calculate cumulative probability of
** each pair of limits
*/
{ p, err, retcode }  = cdfMvn2e(ctl, a, b, corr, nonc);


After the above code, p should equal:

0.04074118
0.31981965
0.13700266


which means that:

$\begin{split}P(-5 \leq x_1 \leq -1 \text{ and } -8 \leq x_2 \leq -1.1) = 0.0407\\ P(-20 \leq x_1 \leq 0 \text{ and } -10 \leq x_2 \leq 0.1) = 0.3198\\ P(0 \leq x_1 \leq 1 \text{ and } 0 \leq x_2 \leq 1.1) = 0.1370\end{split}$

### Compute the non central multivariate normal cdf¶

/*
** Limits of integration
** -5 ≤ x1 ≤ -1 and -8 ≤ x2 ≤ -1.1
** -10 ≤ x1 ≤ 0 and -10 ≤ x2 ≤ 0.1
** 0 ≤ x1 ≤ 1 and 0 ≤ x2 ≤ 1.1
*/
a = { -5  -8,
-20 -10,
0   0 };

b = {  -1 -1.1,
0  0.1,
1  1.1 };

// Correlation matrix
corr = {   1  0.31,
0.31     1 };

// Define non-centrality vector, Kx1
nonc  = {   1,
-2.5 };

// Define control structure
struct cdfmControl ctl;
ctl = cdfmControlCreate();

/*
** Calculate cumulative probability of
** each pair of upper limits
*/
{ p, err, retcode } = cdfMvn2e(ctl, a, b, corr, nonc);


After the above code, p should equal:

0.02246034
0.15854761
0.00094761


which means with non-central vector, the multivariate normal cdf are:

$\begin{split}P(-5 \leq x_1 \leq -1 \text{ and } -8 \leq x_2 \leq -1.1) = 0.0225\\ P(-20 \leq x_1 \leq 0 \text{ and } -10 \leq x_2 \leq 0.1) = 0.1585\\ P(0 \leq x_1 \leq 1 \text{ and } 0 \leq x_2 \leq 1.1) = 0.0009\end{split}$

## Remarks¶

• cdfMvn2e() evaluates the following non-central MVN integral, where $$1\leqslant i \leqslant N$$ where $$z$$ denotes $$K$$ -dimensional multivariate normal distribution, $$\delta$$ denotes the $$K \times 1$$ non-centrality vector with $$-\infty<\:\ \delta_k <\:\ \infty$$ .

• The correlation matrix $$R$$ is defined by $$\Sigma = DRD$$, where $$D$$ denotes the diagonal matrix which has the square roots of the diagonal entries for covariance matrix $$\Sigma$$ on its diagonal.