diff --git a/tupak/core/likelihood.py b/tupak/core/likelihood.py
index d49513604523abeb67f4608057de7069b20095ee..1ac787460f540837f9b3299340def2602481c13e 100644
--- a/tupak/core/likelihood.py
+++ b/tupak/core/likelihood.py
@@ -158,20 +158,26 @@ class GaussianLikelihood(Analytical1DLikelihood):
             .format(self.x, self.y, self.func.__name__, self.sigma)
 
     def log_likelihood(self):
-        return self.__summed_log_likelihood(sigma=self.__get_sigma())
+        log_l = np.sum(- (self.residual / self.sigma)**2 / 2
+                       - np.log(2 * np.pi * self.sigma) / 2)
+        return log_l
 
-    def __get_sigma(self):
+    @property
+    def sigma(self):
         """
         This checks if sigma has been set in parameters. If so, that value
         will be used. Otherwise, the attribute sigma is used. The logic is
         that if sigma is not in parameters the attribute is used which was
         given at init (i.e. the known sigma as either a float or array).
         """
-        return self.parameters.get('sigma', self.sigma)
+        return self.parameters.get('sigma', self._sigma)
 
-    def __summed_log_likelihood(self, sigma):
-        return -0.5 * (np.sum((self.residual / sigma) ** 2) +
-                       self.n * np.log(2 * np.pi * sigma ** 2))
+    @sigma.setter
+    def sigma(self, sigma):
+        if isinstance(sigma, float) or len(sigma) == self.n:
+            self._sigma = sigma
+        else:
+            raise ValueError('Sigma must be either float or array-like x.')
 
 
 class PoissonLikelihood(Analytical1DLikelihood):