NAG CL Interface
g01hbc (prob_​multi_​normal)

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1 Purpose

g01hbc returns the upper tail, lower tail or central probability associated with a multivariate Normal distribution of up to ten dimensions.

2 Specification

#include <nag.h>
double  g01hbc (Nag_TailProbability tail, Integer n, const double a[], const double b[], const double mean[], const double sigma[], Integer tdsig, double tol, Integer maxpts, NagError *fail)
The function may be called by the names: g01hbc, nag_stat_prob_multi_normal or nag_multi_normal.

3 Description

Let the vector random variable X = (X1,X2,,Xn) T follow an n-dimensional multivariate Normal distribution with mean vector μ and n×n variance-covariance matrix Σ, then the probability density function, f(X:μ,Σ), is given by
f(X:μ,Σ) = (2π) - (1/2) n |Σ| -1/2 exp(-12(X-μ)TΣ-1(X-μ)) .  
The lower tail probability is defined by:
P(X1b1,,Xnbn: μ ,Σ)=- b1- bnf(X : μ ,Σ) dXndX1.  
The upper tail probability is defined by:
The central probability is defined by:
P(a1X1b1,,anXnbn: μ ,Σ)=a1b1anbnf(X : μ ,Σ) dXndX1.  
To evaluate the probability for n3, the probability density function of X1,X2,,Xn is considered as the product of the conditional probability of X1,X2,,Xn-2 given Xn-1 and Xn and the marginal bivariate Normal distribution of Xn-1 and Xn. The bivariate Normal probability can be evaluated as described in g01hac and numerical integration is then used over the remaining n-2 dimensions. In the case of n=3, d01sjc is used and for n>3 d01wcc is used.
To evaluate the probability for n=1 a direct call to g01eac is made and for n=2 calls to g01hac are made.

4 References

Kendall M G and Stuart A (1969) The Advanced Theory of Statistics (Volume 1) (3rd Edition) Griffin

5 Arguments

1: tail Nag_TailProbability Input
On entry: indicates which probability is to be returned.
The lower tail probability is returned.
The upper tail probability is returned.
The central probability is returned.
Constraint: tail=Nag_LowerTail, Nag_UpperTail or Nag_Central.
2: n Integer Input
On entry: n, the number of dimensions.
Constraint: 1n10.
3: a[n] const double Input
On entry: if tail=Nag_Central or Nag_UpperTail, the lower bounds, ai, for i=1,2,,n.
If tail=Nag_LowerTail, a is not referenced.
4: b[n] const double Input
On entry: if tail=Nag_Central or Nag_LowerTail, the upper bounds, bi, for i=1,2,,n.
If tail=Nag_UpperTail, b is not referenced.
Constraint: if tail=Nag_Central, a[i-1]<b[i-1], for i=1,2,,n.
5: mean[n] const double Input
On entry: μ, the mean vector of the multivariate Normal distribution.
6: sigma[n×tdsig] const double Input
Note: the (i,j)th element of the matrix is stored in sigma[(i-1)×tdsig+j-1].
On entry: Σ, the variance-covariance matrix of the multivariate Normal distribution. Only the lower triangle is referenced.
Constraint: Σ must be positive definite.
7: tdsig Integer Input
On entry: the stride separating matrix column elements in the array sigma.
Constraint: tdsign.
8: tol double Input
On entry: if n>2 the relative accuracy required for the probability, and if the upper or the lower tail probability is requested then tol is also used to determine the cut-off points, see Section 7.
If n=1, tol is not referenced.
Suggested value: tol=0.0001.
Constraint: if n>1, tol>0.0.
9: maxpts Integer Input
On entry: this argument is no longer referenced, but is included for backwards compatability.
10: fail NagError * Input/Output
The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).

6 Error Indicators and Warnings

If on exit fail.code= NE_2_INT_ARG_LT, NE_2_REAL_ARRAYS_CONS, NE_CHARACTER, NE_INT_ARG_CONS, NE_POS_DEF or NE_REAL_ARG_CONS, then g01hbc returns zero.
On entry, tdsig=value and n=value.
Constraint: tdsign.
On entry, the value value in b is less than or equal to the corresponding value in a.
Full accuracy not achieved, relative accuracy =value. A larger value of tol can be tried or the length of the workspace increased. The returned value is an approximation to the required result.
Dynamic memory allocation failed.
See Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
On entry, argument value had an illegal value.
On entry, n=value.
Constraint: 1n10.
An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
See Section 7.5 in the Introduction to the NAG Library CL Interface for further information.
Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library CL Interface for further information.
On entry, sigma is not positive definite.
On entry, tol=value.
Constraint: tol>0.0.
Accuracy requested by tol is too strict: tol=value. Round-off error has prevented the requested accuracy from being achieved; a larger value of tol can be tried. The returned value will be an approximation to the required result.

7 Accuracy

The accuracy should be as specified by tol. When on exit fail.code= NE_ACC the approximate accuracy achieved is given in the error message. For the upper and lower tail probabilities the infinite limits are approximated by cut-off points for the n-2 dimensions over which the numerical integration takes place; these cut-off points are given by Φ-1(tol/(10×n)), where Φ-1 is the inverse univariate Normal distribution function.

8 Parallelism and Performance

Background information to multithreading can be found in the Multithreading documentation.
g01hbc is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
g01hbc makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.
Please consult the X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this function. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

9 Further Comments

The time taken is related to the number of dimensions, the range over which the integration takes place (bi-ai, for i=1,2,,n) and the value of Σ as well as the accuracy required. As the numerical integration does not take place over the last two dimensions speed may be improved by arranging X so that the largest ranges of integration are for Xn-1 and Xn.

10 Example

This example reads in the mean and covariance matrix for a multivariate Normal distribution and computes and prints the associated central probability.

10.1 Program Text

Program Text (g01hbce.c)

10.2 Program Data

Program Data (g01hbce.d)

10.3 Program Results

Program Results (g01hbce.r)