From b40012517b227daf46e2528b20183150c3f2b6f0 Mon Sep 17 00:00:00 2001
From: Rohan Sikand <57341225+rosikand@users.noreply.github.com>
Date: Tue, 13 Dec 2022 22:06:24 -0500
Subject: [PATCH] Fix parameterization typo

---
 representation/directed/index.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/representation/directed/index.md b/representation/directed/index.md
index 9746ed3..e3426cb 100644
--- a/representation/directed/index.md
+++ b/representation/directed/index.md
@@ -16,9 +16,9 @@ The kinds of models that we will see here are referred to as *Bayesian networks*
 
 ## Probabilistic modeling with Bayesian networks
 
-Directed graphical models (a.k.a. Bayesian networks) are a family of probability distributions that admit a compact parametrization that can be naturally described using a directed graph.
+Directed graphical models (a.k.a. Bayesian networks) are a family of probability distributions that admit a compact parameterization that can be naturally described using a directed graph.
 
-The general idea behind this parametrization is surprisingly simple.
+The general idea behind this parameterization is surprisingly simple.
 
 Recall that by the chain rule, we can write any probability $$p$$ as: