From f947896b688e3081315237ae31cd2d6e254f9364 Mon Sep 17 00:00:00 2001
From: Maxim Ziatdinov <34245227+ziatdinovmax@users.noreply.github.com>
Date: Wed, 27 Nov 2024 15:04:07 -0800
Subject: [PATCH] Update README

---
 README.md | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 6bd64de..df0f74a 100644
--- a/README.md
+++ b/README.md
@@ -61,7 +61,11 @@ model.fit(X_measured, y_measured, num_warmup=1000, num_samples=1000)
 
 ![BNN_PBNN](https://github.com/user-attachments/assets/8281b071-4f05-4432-8e23-babcaaad6b5d)
 
-The obtained posterior means and variances can be used in active learning and Bayesian optimization frameworks. See example of BNN-powered active learning [here](https://github.com/ziatdinovmax/NeuroBayes/blob/main/examples/bnn_example1d.ipynb) and example of PBNN-powered active learning [here](https://github.com/ziatdinovmax/NeuroBayes/blob/main/examples/pbnn_example1d.ipynb).
+The obtained posterior means and variances can be used in active learning and Bayesian optimization frameworks. 
+
+An example of applying PBNN to a toy regression problem can be found [here](https://github.com/ziatdinovmax/NeuroBayes/blob/main/examples/partialBNN_simple_regression.ipynb).
+
+See also examples of BNN-powered active learning [here](https://github.com/ziatdinovmax/NeuroBayes/blob/main/examples/bnn_example1d.ipynb) and PBNN-powered active learning [here](https://github.com/ziatdinovmax/NeuroBayes/blob/main/examples/pbnn_example1d.ipynb).
     
 ### Heteroskedastic noise
 By default, we assume constant observation noise across all inputs. However, this assumption often doesn't hold in real-world datasets which may exhibit input-dependent levels of noise. NeuroBayes offers heteroskedastic BNNs that can capture varying levels of noise in different regions of the data, allowing for more accurate uncertainty quantification.