diff --git a/GPy/kern/Matern32.py b/GPy/kern/Matern32.py
index eacb70ab..3af7ec05 100644
--- a/GPy/kern/Matern32.py
+++ b/GPy/kern/Matern32.py
@@ -14,7 +14,7 @@ class Matern32(kernpart):
 
     .. math::
 
-       k(r) = \sigma^2 (1 + \sqrt{3} r) \exp(- \sqrt{3} r) \\qquad \\qquad \\text{ where  } r = \sqrt{\sum_{i=1}^D \\frac{(x_i-y_i)^2}{\ell_i^2} }
+       k(r) = \sigma^2 (1 + \sqrt{3} r) \exp(- \sqrt{3} r) \ \ \ \ \  \\text{ where  } r = \sqrt{\sum_{i=1}^D \\frac{(x_i-y_i)^2}{\ell_i^2} }
 
     :param D: the number of input dimensions
     :type D: int
diff --git a/GPy/kern/Matern52.py b/GPy/kern/Matern52.py
index c7478653..2994fc45 100644
--- a/GPy/kern/Matern52.py
+++ b/GPy/kern/Matern52.py
@@ -13,7 +13,7 @@ class Matern52(kernpart):
 
     .. math::
 
-       k(r) = \sigma^2 (1 + \sqrt{5} r + \\frac53 r^2) \exp(- \sqrt{5} r) \\qquad \\qquad \\text{ where  } r = \sqrt{\sum_{i=1}^D \\frac{(x_i-y_i)^2}{\ell_i^2} }
+       k(r) = \sigma^2 (1 + \sqrt{5} r + \\frac53 r^2) \exp(- \sqrt{5} r) \ \ \ \ \  \\text{ where  } r = \sqrt{\sum_{i=1}^D \\frac{(x_i-y_i)^2}{\ell_i^2} }
 
     :param D: the number of input dimensions
     :type D: int
diff --git a/GPy/kern/exponential.py b/GPy/kern/exponential.py
index c812dc79..3c9cb192 100644
--- a/GPy/kern/exponential.py
+++ b/GPy/kern/exponential.py
@@ -13,7 +13,7 @@ class exponential(kernpart):
 
     .. math::
 
-       k(r) = \sigma^2 \exp(- r) \\qquad \\qquad \\text{ where  } r = \sqrt{\sum_{i=1}^D \\frac{(x_i-y_i)^2}{\ell_i^2} }
+       k(r) = \sigma^2 \exp(- r) \ \ \ \ \  \\text{ where  } r = \sqrt{\sum_{i=1}^D \\frac{(x_i-y_i)^2}{\ell_i^2} }
 
     :param D: the number of input dimensions
     :type D: int
diff --git a/GPy/kern/rbf.py b/GPy/kern/rbf.py
index 082bbe7f..5babfa4f 100644
--- a/GPy/kern/rbf.py
+++ b/GPy/kern/rbf.py
@@ -12,7 +12,7 @@ class rbf(kernpart):
 
     .. math::
 
-       k(r) = \sigma^2 \exp(- \frac{1}{2}r^2) \\qquad \\qquad \\text{ where  } r^2 = \sum_{i=1}^d \frac{ (x_i-x^\prime_i)^2}{\ell_i^2}}
+       k(r) = \sigma^2 \exp(- \frac{1}{2}r^2) \ \ \ \ \  \\text{ where  } r^2 = \sum_{i=1}^d \frac{ (x_i-x^\prime_i)^2}{\ell_i^2}}
 
     where \ell_i is the lengthscale, \sigma^2 the variance and d the dimensionality of the input.