diff --git a/doc/tuto_GP_regression.rst b/doc/tuto_GP_regression.rst
index fb367848..1a6e245a 100644
--- a/doc/tuto_GP_regression.rst
+++ b/doc/tuto_GP_regression.rst
@@ -7,6 +7,18 @@ Gaussian process regression tutorial
     print "Hello world"
     X = [[1, 10], [1, 20], [1, -2]]
 
+.. plot::
+
+   import matplotlib.pyplot as plt
+   import numpy as np
+   x = np.random.randn(1000)
+   plt.hist( x, 20)
+   plt.grid()
+   plt.title(r'Normal: $\mu=%.2f, \sigma=%.2f$'%(x.mean(), x.std()))
+   plt.show()
+
+
+
 We will see in this tutorial the basics for building a 1 dimensional and a 2 dimensional Gaussian process regression model, also known as a kriging model.
 
 We first import the libraries we will need: ::