Update javadoc examples to use factory method 'of'.
diff --git a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/InverseTransformContinuousSampler.java b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/InverseTransformContinuousSampler.java
index b43ad12..2f5c722 100644
--- a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/InverseTransformContinuousSampler.java
+++ b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/InverseTransformContinuousSampler.java
@@ -42,12 +42,12 @@
* final RealDistribution dist = new ChiSquaredDistribution(9);
* // Create the sampler.
* final ContinuousSampler chiSquareSampler =
- * new InverseTransformContinuousSampler(RandomSource.create(RandomSource.MT),
- * new ContinuousInverseCumulativeProbabilityFunction() {
- * public double inverseCumulativeProbability(double p) {
- * return dist.inverseCumulativeProbability(p);
- * }
- * });
+ * InverseTransformContinuousSampler.of(RandomSource.create(RandomSource.MT),
+ * new ContinuousInverseCumulativeProbabilityFunction() {
+ * public double inverseCumulativeProbability(double p) {
+ * return dist.inverseCumulativeProbability(p);
+ * }
+ * });
*
* // Generate random deviate.
* double random = chiSquareSampler.sample();
diff --git a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/InverseTransformDiscreteSampler.java b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/InverseTransformDiscreteSampler.java
index 3f5991c..fcf04dc 100644
--- a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/InverseTransformDiscreteSampler.java
+++ b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/InverseTransformDiscreteSampler.java
@@ -42,12 +42,12 @@
* final IntegerDistribution dist = new BinomialDistribution(11, 0.56);
* // Create the sampler.
* final DiscreteSampler binomialSampler =
- * new InverseTransformDiscreteSampler(RandomSource.create(RandomSource.MT),
- * new DiscreteInverseCumulativeProbabilityFunction() {
- * public int inverseCumulativeProbability(double p) {
- * return dist.inverseCumulativeProbability(p);
- * }
- * });
+ * InverseTransformDiscreteSampler.of(RandomSource.create(RandomSource.MT),
+ * new DiscreteInverseCumulativeProbabilityFunction() {
+ * public int inverseCumulativeProbability(double p) {
+ * return dist.inverseCumulativeProbability(p);
+ * }
+ * });
*
* // Generate random deviate.
* int random = binomialSampler.sample();
diff --git a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/PoissonSamplerCache.java b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/PoissonSamplerCache.java
index 184f8d5..a294783 100644
--- a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/PoissonSamplerCache.java
+++ b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/PoissonSamplerCache.java
@@ -144,7 +144,7 @@
* Creates a new Poisson sampler.
*
* <p>The returned sampler will function exactly the
- * same as {@link PoissonSampler#PoissonSampler(UniformRandomProvider, double)}.
+ * same as {@link PoissonSampler#of(UniformRandomProvider, double)}.
*
* @param rng Generator of uniformly distributed random numbers.
* @param mean Mean.
