DDE-1/Statistics.md

@@ -110,16 +110,16 @@ Our concern here is, how can we generalize the approach for N number of events?
 
 ```math
 p(E_n|E_m) = p(E_mE_n)/p(E_m)
-...
+```
 
 
 ```math
 p(E_n|E_m) = p(E_m|E_n)p(E_n)/p(E_m)
-...
+```
 
 ```math
 p(E_n|E_m) = p(E_m|E_n)p(E_n)/(\sum_{n=1)^N {p(E_m|E_n)p(E_n)})
-...
+```
 
 This is basically what Bayes’s formula is. It utilized under the hood of several models we will learn throughout the lecture.  Let’s look at an example, for which we have [a visual illustration]( https://seeing-theory.brown.edu/bayesian-inference/index.html#section1) you can play with.