diff --git a/examples/src/main/kotlin/kscience/kmath/commons/prob/DistributionBenchmark.kt b/examples/src/main/kotlin/kscience/kmath/commons/prob/DistributionBenchmark.kt
index 8f68a9d3f..e4a5bc534 100644
--- a/examples/src/main/kotlin/kscience/kmath/commons/prob/DistributionBenchmark.kt
+++ b/examples/src/main/kotlin/kscience/kmath/commons/prob/DistributionBenchmark.kt
@@ -3,19 +3,20 @@ package kscience.kmath.commons.prob
 import kotlinx.coroutines.Dispatchers
 import kotlinx.coroutines.async
 import kotlinx.coroutines.runBlocking
-import org.apache.commons.rng.simple.RandomSource
-import kscience.kmath.prob.RandomChain
 import kscience.kmath.prob.RandomGenerator
+import kscience.kmath.prob.blocking
 import kscience.kmath.prob.fromSource
+import kscience.kmath.prob.samplers.GaussianSampler
+import org.apache.commons.rng.simple.RandomSource
 import java.time.Duration
 import java.time.Instant
-import org.apache.commons.rng.sampling.distribution.ZigguratNormalizedGaussianSampler as ApacheZigguratNormalizedGaussianSampler
-import kscience.kmath.prob.samplers.ZigguratNormalizedGaussianSampler as KMathZigguratNormalizedGaussianSampler
+import org.apache.commons.rng.sampling.distribution.GaussianSampler as CMGaussianSampler
+import org.apache.commons.rng.sampling.distribution.ZigguratNormalizedGaussianSampler as CMZigguratNormalizedGaussianSampler
 
 private suspend fun runKMathChained(): Duration {
     val generator = RandomGenerator.fromSource(RandomSource.MT, 123L)
-    val normal = KMathZigguratNormalizedGaussianSampler.of()
-    val chain = normal.sample(generator) as RandomChain<Double>
+    val normal = GaussianSampler.of(7.0, 2.0)
+    val chain = normal.sample(generator).blocking()
     val startTime = Instant.now()
     var sum = 0.0
 
@@ -28,17 +29,23 @@ private suspend fun runKMathChained(): Duration {
             println("Chain sampler completed $counter elements in $duration: $meanValue")
         }
     }
+
     return Duration.between(startTime, Instant.now())
 }
 
 private fun runApacheDirect(): Duration {
     val rng = RandomSource.create(RandomSource.MT, 123L)
-    val sampler = ApacheZigguratNormalizedGaussianSampler.of<ApacheZigguratNormalizedGaussianSampler>(rng)
+
+    val sampler = CMGaussianSampler.of(
+        CMZigguratNormalizedGaussianSampler.of(rng),
+        7.0,
+        2.0
+    )
+
     val startTime = Instant.now()
-
     var sum = 0.0
-    repeat(10000001) { counter ->
 
+    repeat(10000001) { counter ->
         sum += sampler.sample()
 
         if (counter % 100000 == 0) {
@@ -47,17 +54,16 @@ private fun runApacheDirect(): Duration {
             println("Direct sampler completed $counter elements in $duration: $meanValue")
         }
     }
+
     return Duration.between(startTime, Instant.now())
 }
 
 /**
  * Comparing chain sampling performance with direct sampling performance
  */
-fun main() {
-    runBlocking(Dispatchers.Default) {
-        val chainJob = async { runKMathChained() }
-        val directJob = async { runApacheDirect() }
-        println("KMath Chained: ${chainJob.await()}")
-        println("Apache Direct: ${directJob.await()}")
-    }
+fun main(): Unit = runBlocking(Dispatchers.Default) {
+    val chainJob = async { runKMathChained() }
+    val directJob = async { runApacheDirect() }
+    println("KMath Chained: ${chainJob.await()}")
+    println("Apache Direct: ${directJob.await()}")
 }