diff --git a/doc/GPy.likelihoods.rst b/doc/GPy.likelihoods.rst
index 34672d11..03c15a82 100644
--- a/doc/GPy.likelihoods.rst
+++ b/doc/GPy.likelihoods.rst
@@ -12,7 +12,7 @@ likelihoods Package
 :mod:`EP` Module
 ----------------
 
-.. automodule:: GPy.likelihoods.EP
+.. automodule:: GPy.likelihoods.ep
     :members:
     :undoc-members:
     :show-inheritance:
@@ -20,7 +20,7 @@ likelihoods Package
 :mod:`Gaussian` Module
 ----------------------
 
-.. automodule:: GPy.likelihoods.Gaussian
+.. automodule:: GPy.likelihoods.gaussian
     :members:
     :undoc-members:
     :show-inheritance:
diff --git a/doc/GPy.models.rst b/doc/GPy.models.rst
index 85bd727a..f4ae6a59 100644
--- a/doc/GPy.models.rst
+++ b/doc/GPy.models.rst
@@ -12,71 +12,71 @@ models Package
 :mod:`Bayesian_GPLVM` Module
 ----------------------------
 
-.. automodule:: GPy.models.Bayesian_GPLVM
+.. automodule:: GPy.models.bayesian_gplvm
     :members:
     :undoc-members:
     :show-inheritance:
 
-:mod:`GP` Module
+:mod:`gp` Module
 ----------------
 
-.. automodule:: GPy.models.GP
+.. automodule:: GPy.models.gp
     :members:
     :undoc-members:
     :show-inheritance:
 
-:mod:`GPLVM` Module
+:mod:`gplvm` Module
 -------------------
 
-.. automodule:: GPy.models.GPLVM
+.. automodule:: GPy.models.gplvm
     :members:
     :undoc-members:
     :show-inheritance:
 
-:mod:`GP_regression` Module
+:mod:`gp_regression` Module
 ---------------------------
 
-.. automodule:: GPy.models.GP_regression
+.. automodule:: GPy.models.gp_regression
     :members:
     :undoc-members:
     :show-inheritance:
 
-:mod:`sparse_GP` Module
+:mod:`sparse_gp` Module
 -----------------------
 
-.. automodule:: GPy.models.sparse_GP
+.. automodule:: GPy.models.sparse_gp
     :members:
     :undoc-members:
     :show-inheritance:
 
-:mod:`sparse_GPLVM` Module
+:mod:`SparseGPLVM` Module
 --------------------------
 
-.. automodule:: GPy.models.sparse_GPLVM
+.. automodule:: GPy.models.sparse_gplvm
     :members:
     :undoc-members:
     :show-inheritance:
 
-:mod:`sparse_GP_regression` Module
+:mod:`sparse_gp_regression` Module
 ----------------------------------
 
-.. automodule:: GPy.models.sparse_GP_regression
+.. automodule:: GPy.models.sparse_gp_regression
     :members:
     :undoc-members:
     :show-inheritance:
 
-:mod:`uncollapsed_sparse_GP` Module
------------------------------------
+.. :mod:`uncollapsed_sparse_GP` Module
+.. -----------------------------------
 
-.. automodule:: GPy.models.uncollapsed_sparse_GP
-    :members:
-    :undoc-members:
-    :show-inheritance:
+.. .. automodule:: GPy.models.uncollapsed_sparse_GP
+..     :members:
+..     :undoc-members:
+..     :show-inheritance:
 
-:mod:`warped_GP` Module
+:mod:`warped_gp` Module
 -----------------------
 
-.. automodule:: GPy.models.warped_GP
+.. automodule:: GPy.models.warped_gp
     :members:
     :undoc-members:
     :show-inheritance:
diff --git a/doc/tuto_GP_regression.rst b/doc/tuto_GP_regression.rst
index 9f01de93..3d3ab10a 100644
--- a/doc/tuto_GP_regression.rst
+++ b/doc/tuto_GP_regression.rst
@@ -36,7 +36,7 @@ The parameter ``input_dim`` stands for the dimension of the input space. The par
 
 The inputs required for building the model are the observations and the kernel::
 
-    m = GPy.models.GP_regression(X,Y,kernel)
+    m = GPy.models.GPRegression(X,Y,kernel)
 
 By default, some observation noise is added to the modle. The functions ``print`` and ``plot`` give an insight of the model we have just build. The code::
 
@@ -116,7 +116,7 @@ Here is a 2 dimensional example::
     ker = GPy.kern.Matern52(2,ARD=True) + GPy.kern.white(2)
 
     # create simple GP model
-    m = GPy.models.GP_regression(X,Y,ker)
+    m = GPy.models.GPRegression(X,Y,ker)
 
     # contrain all parameters to be positive
     m.constrain_positive('')
diff --git a/doc/tuto_kernel_overview.rst b/doc/tuto_kernel_overview.rst
index 450e53e2..e9e8f290 100644
--- a/doc/tuto_kernel_overview.rst
+++ b/doc/tuto_kernel_overview.rst
@@ -211,7 +211,7 @@ Note the ties between the parameters of ``Kanova`` that reflect the links betwee
     Y = 0.5*X[:,:1] + 0.5*X[:,1:] + 2*np.sin(X[:,:1]) * np.sin(X[:,1:])
 
     # Create GP regression model
-    m = GPy.models.GP_regression(X,Y,Kanova)
+    m = GPy.models.GPRegression(X,Y,Kanova)
     m.plot()
 
 .. figure::  Figures/tuto_kern_overview_mANOVA.png