From 8619dc2b7a43d10dd34d9a93534a468e1717f2a5 Mon Sep 17 00:00:00 2001
From: Aidan Scannell <scannell.aidan@gmail.com>
Date: Fri, 9 Feb 2024 17:09:17 +0200
Subject: [PATCH] switched order of benefits

---
 index.markdown | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/index.markdown b/index.markdown
index 63178d6..a0ddc53 100644
--- a/index.markdown
+++ b/index.markdown
@@ -59,9 +59,9 @@ author: "<a href='https://www.aidanscannell.com/'>Aidan Scannell</a><sup>*</sup>
 - `SFR` can be viewed as a function-space Laplace approximation for NNs
 - `SFR` has several benefits over [weight-space Laplace approximation for NNs](https://arxiv.org/abs/2106.14806):
     - Its function-space representation is effective for regularization in continual learning (CL)
-    - It can incorporate new data without retraining the NN
     - It has good uncertainty estimates
         - We use them to guide exploration in model-based reinforcement learning (RL)
+    - It can incorporate new data without retraining the NN
     <!-- - It learns expressive covariance structures from data -->
         <!-- - Not limited by stat -->