diff --git a/lib/test/t_ExperimentIntegration_save.cxx b/lib/test/t_ExperimentIntegration_save.cxx
index ff2baec182..0195bccbde 100644
--- a/lib/test/t_ExperimentIntegration_save.cxx
+++ b/lib/test/t_ExperimentIntegration_save.cxx
@@ -34,7 +34,7 @@ int main(int, char *[])
   const UnsignedInteger dimension = 3;
   fullprint << "Create the input distribution" << std::endl;
   const Collection<Distribution> marginals(dimension, Uniform(-M_PI, M_PI));
-  const ComposedDistribution distributionIshigami(marginals);
+  const JointDistribution distributionIshigami(marginals);
   const UnsignedInteger sampleSize = 100;
   const MonteCarloExperiment experiment2(distributionIshigami, sampleSize);
   const ExperimentIntegration integration(experiment2);
diff --git a/lib/test/t_ExperimentIntegration_std.cxx b/lib/test/t_ExperimentIntegration_std.cxx
index 58b350e87a..f8b33f1a8f 100644
--- a/lib/test/t_ExperimentIntegration_std.cxx
+++ b/lib/test/t_ExperimentIntegration_std.cxx
@@ -52,7 +52,7 @@ int main(int, char *[])
   // Create the input distribution
   fullprint << "Create the input distribution" << std::endl;
   const Collection<Distribution> marginals(dimension, Uniform(-M_PI, M_PI));
-  const ComposedDistribution distributionIshigami(marginals);
+  const JointDistribution distributionIshigami(marginals);
 
   const UnsignedInteger sampleSize = 1000000;
   const MonteCarloExperiment experiment2(distributionIshigami, sampleSize);
diff --git a/python/doc/examples/graphs/plot_graphs_contour.py b/python/doc/examples/graphs/plot_graphs_contour.py
index 5f9fc05071..bf4be8c005 100644
--- a/python/doc/examples/graphs/plot_graphs_contour.py
+++ b/python/doc/examples/graphs/plot_graphs_contour.py
@@ -122,11 +122,11 @@
 copula = ot.NormalCopula(corr)
 x1 = ot.Normal(-1.0, 1)
 x2 = ot.Normal(2, 1)
-x_funk = ot.ComposedDistribution([x1, x2], copula)
+x_funk = ot.JointDistribution([x1, x2], copula)
 
 x1 = ot.Normal(1.0, 1)
 x2 = ot.Normal(-2, 1)
-x_punk = ot.ComposedDistribution([x1, x2], copula)
+x_punk = ot.JointDistribution([x1, x2], copula)
 mixture = ot.Mixture([x_funk, x_punk], [0.5, 1.0])
 
 # %%
diff --git a/python/src/FunctionalChaosAlgorithm_doc.i.in b/python/src/FunctionalChaosAlgorithm_doc.i.in
index 2e3b30e339..ac13aa89ad 100644
--- a/python/src/FunctionalChaosAlgorithm_doc.i.in
+++ b/python/src/FunctionalChaosAlgorithm_doc.i.in
@@ -90,7 +90,7 @@ Create the model:
 >>> ot.RandomGenerator.SetSeed(0)
 >>> inputDimension = 1
 >>> model = ot.SymbolicFunction(['x'], ['x * sin(x)'])
->>> distribution = ot.ComposedDistribution([ot.Uniform()] * inputDimension)
+>>> distribution = ot.JointDistribution([ot.Uniform()] * inputDimension)
 
 Build the multivariate orthonormal basis:
 
diff --git a/python/src/FunctionalChaosRandomVector_doc.i.in b/python/src/FunctionalChaosRandomVector_doc.i.in
index 2ada84ca74..286566ae3f 100644
--- a/python/src/FunctionalChaosRandomVector_doc.i.in
+++ b/python/src/FunctionalChaosRandomVector_doc.i.in
@@ -59,7 +59,7 @@ First, we create the PCE.
 >>> ot.RandomGenerator.SetSeed(0)
 >>> inputDimension = 1
 >>> model = ot.SymbolicFunction(['x'], ['x * sin(x)'])
->>> distribution = ot.ComposedDistribution([ot.Uniform()] * inputDimension)
+>>> distribution = ot.JointDistribution([ot.Uniform()] * inputDimension)
 >>> polyColl = [0.0] * inputDimension
 >>> for i in range(distribution.getDimension()):
 ...     polyColl[i] = ot.StandardDistributionPolynomialFactory(distribution.getMarginal(i))