diff --git a/_posts/2024-05-07-elaborating-on-the-value-of-flow-matching-for-density-estimation.md b/_posts/2024-05-07-elaborating-on-the-value-of-flow-matching-for-density-estimation.md
index ec6d4a56..955d06d0 100644
--- a/_posts/2024-05-07-elaborating-on-the-value-of-flow-matching-for-density-estimation.md
+++ b/_posts/2024-05-07-elaborating-on-the-value-of-flow-matching-for-density-estimation.md
@@ -76,15 +76,21 @@ distributions by transforming a simple, known distribution into a more complex
one. They do so by leveraging the change of variables formula, defining a
bijection from the simple distribution to the complex one.
-For most of the time, the standard definition of flows, achieving the notable
-results, was based on chaining several differentiable and invertible
-transformations. However, these diffeomorphic transformations limit the flows in
-their complexity as such have to be simple. Furthermore, this leads to trade-off
-sampling speed and evaluation performance . Their continuous couterpart,
Continuous Normalizing Flows (CNFs) have been held back by limitations in their
simulation-based maximum likelihood training .
+key="tong_improving_2023">. By utilizing Flow Matching, this limitation
+has been overcome and CNFs have been shown to be a powerful tool for density
+estimation.
+
+In the following sections, CNFs and Flow Matching are explained. Following the
+explanation, the empirical results of Flow Matching are presented. Finally, the
+application of Flow Matching in Simulation-based Inference is discussed, which
+shall highlight their wide applicability and consistent improvement.
# Continuous Normalizing Flows