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-rw-r--r--posts/2019-02-14-raise-your-elbo.md2
1 files changed, 1 insertions, 1 deletions
diff --git a/posts/2019-02-14-raise-your-elbo.md b/posts/2019-02-14-raise-your-elbo.md
index 18c085b..5b789aa 100644
--- a/posts/2019-02-14-raise-your-elbo.md
+++ b/posts/2019-02-14-raise-your-elbo.md
@@ -550,7 +550,7 @@ this section). But now both $\pi$ and $\eta$ are random variables. Let
the prior distribution $p(\pi)$ is Dirichlet with parameter
$(\alpha, \alpha, ..., \alpha)$. Let the prior $p(\eta_k)$ be the
conjugate prior of $(x | \eta_k)$, with parameter $\beta$, we will see
-later in this section that the posterior $q(\eta_k)$ has belongs to the
+later in this section that the posterior $q(\eta_k)$ belongs to the
same family as $p(\eta_k)$. Represented in a plate notations, a fully
Bayesian mixture model looks like: