Apache
/
commons-math
mirror of https://github.com/apache/commons-math.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

added an entry for Pascal distribution 

fixed typos
fixed a code example

git-svn-id: https://svn.apache.org/repos/asf/jakarta/commons/proper/math/trunk@514875 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Luc Maisonobe 2007-03-05 21:35:31 +00:00
parent 358973c257
commit 96784db88a
1 changed files with 5 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

9
xdocs/userguide/distribution.xml
View File

@ -60,11 +60,12 @@ BinomialDistribution binomial = factory.createBinomialDistribution(10, .75);</so
<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>createHypogeometricDistribution</td><td><div>Population size</div><div>Number of successes in population</div><div>Sample size</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>
@ -74,13 +75,13 @@ BinomialDistribution binomial = factory.createBinomialDistribution(10, .75);</so
<code>P(X &lt;= x)</code> (i.e. the lower tail probability of <code>X</code>).
</p>
<source>DistributionFactory factory = DistributionFactory.newInstance();
TDistribution t = factory.createBinomialDistribution(29);
TDistribution t = factory.createTDistribution(29);
double lowerTail = t.cumulativeProbability(-2.656); // P(T &lt;= -2.656)
double upperTail = 1.0 - t.cumulativeProbability(2.75); // P(T &gt;= 2.75)</source>
<p>
The inverse PDF and CDF values are just as easily computed using the
<code>inverseCumulativeProbability</code>methods. For a distribution <code>X</code>,
and a probability, <code>p</code>, <code>inverseCumulativeProbability</code>
<code>inverseCumulativeProbability</code> methods. For a distribution <code>X</code>,
and a probability, <code>p</code>, <code>inverseCumulativeProbability</code>
computes the domain value <code>x</code>, such that:
<ul>
<li><code>P(X &lt;= x) = p</code>, for continuous distributions</li>