diff --git a/Chapter6_Priorities/Chapter6.ipynb b/Chapter6_Priorities/Chapter6.ipynb
index 45779dfd..f18ae782 100644
--- a/Chapter6_Priorities/Chapter6.ipynb
+++ b/Chapter6_Priorities/Chapter6.ipynb
@@ -141,8 +141,9 @@
     "A very simple example follows: suppose we wish to estimate the parameter $\\mu$ of a Normal distribution, with $\\sigma = 5$. Since $\\mu$ could range over the whole real line, we can use a Normal distribution as a prior for $\\mu$. How to select the prior's hyperparameters, denoted ($\\mu_p, \\sigma_p^2$)? The $\\sigma_p^2$ parameter can be chosen to reflect the uncertainty we have. For $\\mu_p$, we have two options:\n",
     "\n",
     "1. Empirical Bayes suggests using the empirical sample mean, which will center the prior around the observed empirical mean:\n",
-    "\n",
-    "$$ \\mu_p = \\frac{1}{N} \\sum_{i=0}^N X_i $$\n",
+    "$$\n",
+    "\\mu_p = \\frac{1}{N} \\sum_{i=0}^N X_i \n",
+    "$$\n",
     "\n",
     "2. Traditional Bayesian inference suggests using prior knowledge, or a more objective prior (zero mean and fat standard deviation).\n",
     "\n",