DDE-1/Statistics.md

@@ -118,7 +118,7 @@ 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)/
+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.