# lhsnorm

Latin hypercube sample from multivariate normal distribution

## Description

returns a numeric matrix `X`

= lhsnorm(`mu`

,`sigma`

,`n`

)`X`

containing a Latin hypercube sample of size
`n`

from a multivariate normal distribution with mean vector
`mu`

and covariance matrix `sigma`

. The size of
`X`

is `n`

-by-*d*, where
*d* is the size of `mu`

. `X`

is
similar to a random sample generated from the multivariate normal distribution (see
`mvnrnd`

), but `lhsnorm`

adjusts the marginal
distribution of each column so that its sample marginal distribution is close to its
theoretical normal distribution.

## Examples

## Input Arguments

## Output Arguments

## Tips

`lhsnorm`

requires the covariance matrix`sigma`

to be symmetric. If`sigma`

has only minor asymmetry, you can use`(sigma + sigma')/2`

to resolve the asymmetry.

## References

## Version History

**Introduced before R2006a**