This is an automated email from the ASF dual-hosted git repository.
aherbert pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/commons-rng.git
commit bacb5d654343e548837203ef3b19b75f423ed191
Author: aherbert <aherb...@apache.org>
AuthorDate: Thu Feb 14 15:23:31 2019 +0000
Changed equations to use MathJax notation.
---
.../rng/sampling/distribution/GeometricSampler.java | 14 +++++++-------
1 file changed, 7 insertions(+), 7 deletions(-)
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/GeometricSampler.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/GeometricSampler.java
index 757367d..92b8df2 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/GeometricSampler.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/GeometricSampler.java
@@ -25,17 +25,17 @@ import org.apache.commons.rng.UniformRandomProvider;
* <p>This distribution samples the number of failures before the first
success taking values in the
* set {@code [0, 1, 2, ...]}.
*
- * <p>The sample is computed using a related an exponential distribution. If
{@code X} is an
- * exponentially distributed random variable with parameter λ, then {@code Y =
floor(X)} is a
- * geometrically distributed random variable with parameter p = 1 −
e<sup>−λ</sup>, with {@code p}
- * the probability of success.
+ * <p>The sample is computed using a related exponential distribution. If \( X
\) is an
+ * exponentially distributed random variable with parameter \( \lambda \), then
+ * \( Y = \left \lfloor X \right \rfloor \) is a geometrically distributed
random variable with
+ * parameter \( p = 1 − e^\lambda \), with \( p \) the probability of success.
*
* <p>This sampler outperforms using the {@link
InverseTransformDiscreteSampler} with an appropriate
* Geometric inverse cumulative probability function.
*
- * <p>Usage note: As the probability of success ({@code p}) tends towards zero
the mean of the
- * distribution ({@code (1-p)/p}) tends towards infinity and due to the use of
{@code int} for the
- * sample this can result in truncation of the distribution.
+ * <p>Usage note: As the probability of success (\( p \)) tends towards zero
the mean of the
+ * distribution (\( \frac{1-p}{p} \)) tends towards infinity and due to the
use of {@code int}
+ * for the sample this can result in truncation of the distribution.
*
* @see <a
*
href="https://en.wikipedia.org/wiki/Geometric_distribution#Related_distributions";>Geometric
