sblunt · eshelDror · Jul 3, 2026 · Jul 3, 2026 · Jul 3, 2026 · Jul 3, 2026
diff --git a/docs/tutorials/Changing_bases_tutorial.ipynb b/docs/tutorials/Changing_bases_tutorial.ipynb
diff --git a/docs/tutorials/HGCA_tutorial.ipynb b/docs/tutorials/HGCA_tutorial.ipynb
diff --git a/docs/tutorials/Hipparcos_IAD.ipynb b/docs/tutorials/Hipparcos_IAD.ipynb
diff --git a/docs/tutorials/MCMC_tutorial.ipynb b/docs/tutorials/MCMC_tutorial.ipynb
diff --git a/docs/tutorials/Multiplanet_Tutorial.ipynb b/docs/tutorials/Multiplanet_Tutorial.ipynb
diff --git a/docs/tutorials/OFTI_tutorial.ipynb b/docs/tutorials/OFTI_tutorial.ipynb
diff --git a/docs/tutorials/ONeil-ObsPriors.ipynb b/docs/tutorials/ONeil-ObsPriors.ipynb
diff --git a/docs/tutorials/Plotting_tutorial.ipynb b/docs/tutorials/Plotting_tutorial.ipynb
diff --git a/docs/tutorials/Quick-Start.ipynb b/docs/tutorials/Quick-Start.ipynb
diff --git a/docs/tutorials/RV_MCMC_Tutorial.ipynb b/docs/tutorials/RV_MCMC_Tutorial.ipynb
diff --git a/orbitize/plot.py b/orbitize/plot.py
diff --git a/orbitize/priors.py b/orbitize/priors.py
@@ -270,6 +270,7 @@ def __init__(self, mu, sigma, no_negatives=True):
         self.mu = mu
         self.sigma = sigma
         self.no_negatives = no_negatives
+        self.lnnorm = -(np.log(sigma)+0.5*np.log(2*np.pi))
 
     def __repr__(self):
         return "Gaussian"
@@ -330,7 +331,7 @@ def compute_lnprob(self, element_array):
             corresponding to the probability of drawing each of the numbers
             in the input `element_array`.
         """
-        lnprob = -0.5 * ((element_array - self.mu) / self.sigma) ** 2
+        lnprob = -0.5 * ((element_array - self.mu) / self.sigma) ** 2 + self.lnnorm
 
         if self.no_negatives:
             bad_samples = np.where(element_array < 0)[0]

diff --git a/tests/test_priors.py b/tests/test_priors.py
@@ -40,8 +40,8 @@
         [
             0.0,
             0.0,
-            nm(1000.0, 1.0).pdf(1000.0) * np.sqrt(2 * np.pi),
-            nm(1000.0, 1.0).pdf(999.0) * np.sqrt(2 * np.pi),
+            nm(1000.0, 1.0).pdf(1000.0),
+            nm(1000.0, 1.0).pdf(999.0),
         ]
     ),
     priors.LogUniformPrior: np.array([0.0, 0.0, 1.0, 2.0 / 3.0, 0.5, 0.0]) / np.log(2),