diff --git a/Zettelkasten/Fleeting Notes/Class/Bayesian Signal Processing/20260121.md b/Zettelkasten/Fleeting Notes/Class/Bayesian Signal Processing/20260121.md
index 89471f7a1..36a3ab304 100644
--- a/Zettelkasten/Fleeting Notes/Class/Bayesian Signal Processing/20260121.md	
+++ b/Zettelkasten/Fleeting Notes/Class/Bayesian Signal Processing/20260121.md	
@@ -91,9 +91,15 @@ if $B = A_i$, $P(A_j|A_i) = 1$ when i = j, 0 otherwise
 > Bayesian statistics are a way of thinking about a **degree
 > of belief** in a probability, not an estimation of the
 > probability from a number of experiments.
+> 
+> We know:
 >
 > $P(AB) = P(A|B)P(B) = P(B|A)P(A)$
 >
 > Then Bayes Theorem becomes
-> $P(A|B) = \frac{P(B|A) P(A)}{P(B)}$
+> $$P(A|B) = \frac{P(B|A) P(A)}{P(B)}$$
+>
+> and then when events $A_i$ are mutually exclusive...
+>
+> $$P(A_i|B) = \frac{P(B|A_i) P(A_i)}{\sum_i P(B|A_i) P(A_i)}$$
 >