Apache/commons-math
1
0
Fork 0
mirror of https://github.com/apache/commons-math.git synced 2025-02-07 10:38:55 +00:00
Code Issues Packages Projects Releases Wiki Activity

Fixed javadoc typos.

Browse Source 
git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@1043908 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Phil Steitz 2010-12-09 11:53:14 +00:00
parent 5d2b33bd45
commit d393072e58
1 changed files with 2 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
src/main/java/org/apache/commons/math/random/CorrelatedRandomVectorGenerator.java
View File

@ -36,7 +36,7 @@ import org.apache.commons.math.util.FastMath;
* interesting case is when the generated vector should be drawn from a <a
* href="http://en.wikipedia.org/wiki/Multivariate_normal_distribution">
* Multivariate Normal Distribution</a>. The approach using a Cholesky
* decomposition is quite usual in this case. However, it cas be extended
* decomposition is quite usual in this case. However, it can be extended
* to other cases as long as the underlying random generator provides
* {@link NormalizedRandomGenerator normalized values} like {@link
* GaussianRandomGenerator} or {@link UniformRandomGenerator}.</p>
@ -48,7 +48,7 @@ import org.apache.commons.math.util.FastMath;
* should be null. Another non-conventional extension handling this case
* is used here. Rather than computing <code>C = U<sup>T</sup>.U</code>
* where <code>C</code> is the covariance matrix and <code>U</code>
* is an uppertriangular matrix, we compute <code>C = B.B<sup>T</sup></code>
* is an upper-triangular matrix, we compute <code>C = B.B<sup>T</sup></code>
* where <code>B</code> is a rectangular matrix having
* more rows than columns. The number of columns of <code>B</code> is
* the rank of the covariance matrix, and it is the dimension of the