Author: luc
Date: Fri Jan 4 07:41:38 2008
New Revision: 608890
URL: http://svn.apache.org/viewvc?rev=608890&view=rev
Log:
fixed typos
Modified:
commons/proper/math/trunk/xdocs/userguide/random.xml
Modified: commons/proper/math/trunk/xdocs/userguide/random.xml
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/xdocs/userguide/random.xml?rev=608890&r1=608889&r2=608890&view=diff
==============================================================================
--- commons/proper/math/trunk/xdocs/userguide/random.xml (original)
+++ commons/proper/math/trunk/xdocs/userguide/random.xml Fri Jan 4 07:41:38
2008
@@ -117,7 +117,7 @@
For the non-secure methods, starting with the same seed always produces the
same sequence of values. Secure sequences started with the same seeds will
diverge. When a new <code>RandomDataImpl</code> is created, the underlying
- random number generators are <strong>not</strong> intialized. The first
+ random number generators are <strong>not</strong> initialized. The first
call to a data generation method, or to a <code>reSeed()</code> method
initializes the appropriate generator. If you do not explicitly seed the
generator, it is by default seeded with the current time in milliseconds.
@@ -176,15 +176,15 @@
however, generating them is much more difficult. The <a
href="../apidocs/org/apache/commons/math/random/CorrelatedRandomVectorGenerator.html">
org.apache.commons.math.CorrelatedRandomVectorGenerator</a> class
- provides this service. In this case, the user must set a complete
covariance matrix
- instead of a simple standard deviations vector, this matrix gather both
the variance
+ provides this service. In this case, the user must set up a complete
covariance matrix
+ instead of a simple standard deviations vector. This matrix gathers both
the variance
and the correlation information of the probability law.
</p>
<p>
The main use for correlated random vector generation is for Monte-Carlo
simulation of physical problems with several variables, for example to
generate error vectors to be added to a nominal vector. A particularly
- interesting case is when the generated vector should be drawn from a <a
+ common 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>.
</p>
@@ -226,7 +226,7 @@
To select a random sample of objects in a collection, you can use the
<code>nextSample</code> method in the <code>RandomData</code> interface.
Specifically, if <code>c</code> is a collection containing at least
- <code>k</code> objects, and <code>ranomData</code> is a
+ <code>k</code> objects, and <code>randomData</code> is a
<code>RandomData</code> instance <code>randomData.nextSample(c, k)</code>
will return an <code>object[]</code> array of length <code>k</code>
consisting of elements randomly selected from the collection. If
