Author: luc
Date: Wed Sep 10 12:53:40 2008
New Revision: 693957
URL: http://svn.apache.org/viewvc?rev=693957&view=rev
Log:
distribution factory was removed after 1.2 release
Modified:
commons/proper/math/branches/MATH_2_0/src/site/xdoc/userguide/distribution.xml
Modified:
commons/proper/math/branches/MATH_2_0/src/site/xdoc/userguide/distribution.xml
URL:
http://svn.apache.org/viewvc/commons/proper/math/branches/MATH_2_0/src/site/xdoc/userguide/distribution.xml?rev=693957&r1=693956&r2=693957&view=diff
==============================================================================
---
commons/proper/math/branches/MATH_2_0/src/site/xdoc/userguide/distribution.xml
(original)
+++
commons/proper/math/branches/MATH_2_0/src/site/xdoc/userguide/distribution.xml
Wed Sep 10 12:53:40 2008
@@ -40,35 +40,6 @@
computation of inverse PDF and inverse CDF values.
</p>
<p>
- In order to use the distribution framework, first a distribution
object must
- be created. It is encouraged that all distribution object creation
occurs via
- the
<code>org.apache.commons.math.distribution.DistributionFactory</code>
- class. <code>DistributionFactory</code> is a simple factory used to
create all
- of the distribution objects supported by Commons-Math. The typical
usage of
- <code>DistributionFactory</code> to create a distribution object
would be:
- </p>
- <source>DistributionFactory factory =
DistributionFactory.newInstance();
-BinomialDistribution binomial = factory.createBinomialDistribution(10,
.75);</source>
- <p>
- The distributions that can be instantiated via the
<code>DistributionFactory</code>
- are detailed below:
- <table>
- <tr><th>Distribution</th><th>Factory
Method</th><th>Parameters</th></tr>
-
<tr><td>Binomial</td><td>createBinomialDistribution</td><td><div>Number of
trials</div><div>Probability of success</div></td></tr>
-
<tr><td>Cauchy</td><td>createCauchyDistribution</td><td><div>Median</div><div>Scale</div></td></tr>
-
<tr><td>Chi-Squared</td><td>createChiSquaredDistribution</td><td><div>Degrees
of freedom</div></td></tr>
-
<tr><td>Exponential</td><td>createExponentialDistribution</td><td><div>Mean</div></td></tr>
- <tr><td>F</td><td>createFDistribution</td><td><div>Numerator
degrees of freedom</div><div>Denominator degrees of freedom</div></td></tr>
-
<tr><td>Gamma</td><td>createGammaDistribution</td><td><div>Alpha</div><div>Beta</div></td></tr>
-
<tr><td>Hypergeometric</td><td>createHypergeometricDistribution</td><td><div>Population
size</div><div>Number of successes in population</div><div>Sample
size</div></td></tr>
- <tr><td>Normal
(Gaussian)</td><td>createNormalDistribution</td><td><div>Mean</div><div>Standard
Deviation</div></td></tr>
-
<tr><td>Poisson</td><td>createPoissonDistribution</td><td><div>Mean</div></td></tr>
- <tr><td>t</td><td>createTDistribution</td><td><div>Degrees of
freedom</div></td></tr>
-
<tr><td>Weibull</td><td>createWeibullDistribution</td><td><div>Shape</div><div>Scale</div><div>Location</div></td></tr>
-
<tr><td>Pascal</td><td>createPascalDistribution</td><td><div>numberOfSuccesses</div><div>probabilityOfSuccess</div></td></tr>
- </table>
- </p>
- <p>
Using a distribution object, PDF and CDF probabilities are easily
computed
using the <code>cumulativeProbability</code> methods. For a
distribution <code>X</code>,
and a domain value, <code>x</code>,
<code>cumulativeProbability</code> computes
