From 2f652bd8463088bf4d706d247a697200ddf37a30 Mon Sep 17 00:00:00 2001
From: Jakob Robnik <43053552+JakobRobnik@users.noreply.github.com>
Date: Fri, 1 Dec 2023 21:51:07 +0100
Subject: [PATCH] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 4b181ed..e85b15e 100644
--- a/README.md
+++ b/README.md
@@ -31,7 +31,7 @@ Check out [this tutorial](notebooks/tutorials/Constraints.ipynb).
 MCHMC samples are less costly. What is relevant for the computational time is the number of gradient evaluations used. Each sample in MCHMC is two gradient evaluations (1 gradient evaluation if leapfrog integrator is used instead of minimal norm integrator). Each sample in HMC is L gradient evaluations (where L is the number of leapfrog steps per sample), which can be quite large for hard targets (in default NUTS setting up to 1024).
 
 ### Is MCHMC just some weird projection of HMC onto the constant energy surface?
-No, the Hamiltonian dynamics of both methods are different (the particles move differently).
+No, the Hamiltonian dynamics of both methods are different (the particles move differently). Below is the motion of MCHMC particles for the Rosenbrock target distribution.