rst files from documentation

doc/GPy.kern.rst

@@ -89,18 +89,34 @@ kern Package
     :undoc-members:
     :show-inheritance:
 
-:mod:`linear_ARD` Module
-------------------------
+:mod:`periodic_Matern32` Module
+-------------------------------
 
-.. automodule:: GPy.kern.linear_ARD
+.. automodule:: GPy.kern.periodic_Matern32
     :members:
     :undoc-members:
     :show-inheritance:
 
-:mod:`rbf-testing` Module
--------------------------
+:mod:`periodic_Matern52` Module
+-------------------------------
 
-.. automodule:: GPy.kern.rbf-testing
+.. automodule:: GPy.kern.periodic_Matern52
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+:mod:`periodic_exponential` Module
+----------------------------------
+
+.. automodule:: GPy.kern.periodic_exponential
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+:mod:`product_orthogonal` Module
+--------------------------------
+
+.. automodule:: GPy.kern.product_orthogonal
     :members:
     :undoc-members:
     :show-inheritance:

doc/GPy.models.rst

@@ -9,6 +9,14 @@ models Package
     :undoc-members:
     :show-inheritance:
 
+:mod:`BGPLVM` Module
+--------------------
+
+.. automodule:: GPy.models.BGPLVM
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 :mod:`GPLVM` Module
 -------------------
 

doc/GPy.rst

@@ -15,6 +15,7 @@ Subpackages
 .. toctree::
 
     GPy.core
+    GPy.examples
     GPy.inference
     GPy.kern
     GPy.models

doc/tuto_GP_regression.rst

@@ -3,7 +3,7 @@
 Gaussian process regression tutorial
 *************************************
 
-We will see in this tutorial the basics for building a 1 dimensional and a 2 dimensional Gaussian process model, also known as a kriging model.
+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: ::
 
@@ -61,7 +61,7 @@ gives the following output: ::
 
     GP regression model before optimization of the parameters. The shaded region corresponds to 95% confidence intervals (ie +/- 2 standard deviation).
 
-The default values of the kernel parameters may not be relevant for the current data (for example, the confidence intervals seems too wide on the previous figure). A common approach is find the values of the parameters that maximize the likelihood of the data. There are two steps for doing that with GPy:
+The default values of the kernel parameters may not be relevant for the current data (for example, the confidence intervals seems too wide on the previous figure). A common approach is to find the values of the parameters that maximize the likelihood of the data. There are two steps for doing that with GPy:
 
 * Constrain the parameters of the kernel to ensure the kernel will always be a valid covariance structure (For example, we don\'t want some variances to be negative!).
 * Run the optimization