From 2dc72911708970cf074e1a770d3b659dfb312de3 Mon Sep 17 00:00:00 2001
From: Yury Kaminsky <86363785+jrzkaminski@users.noreply.github.com>
Date: Mon, 17 Jul 2023 20:39:33 +0300
Subject: [PATCH] Update README.rst

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

diff --git a/README.rst b/README.rst
index cd777fa..b1c6969 100644
--- a/README.rst
+++ b/README.rst
@@ -44,7 +44,7 @@ The following algorithms for Bayesian Networks learning are implemented:
 
 
 * Building the structure of a Bayesian network based on expert knowledge by directly specifying the structure of the network;
-* Building the structure of a Bayesian network on data using three algorithms - Hill Climbing, evolutionary and PC (evolutionary and PC are currently under development). For Hill Climbing, the following score functions are implemented - MI, K2, BIC, AIC. The algorithms work on both discrete and mixed data.
+* Building the structure of a Bayesian network on data using three algorithms - Hill Climbing, evolutionary and PC (PC is currently under development). For Hill Climbing, the following score functions are implemented - MI, K2, BIC, AIC. The algorithms work on both discrete and mixed data.
 * Learning the parameters of distributions in the nodes of the network based on Gaussian distribution and Mixture Gaussian distribution with automatic selection of the number of components. 
 * Non-parametric learning of distributions at nodes using classification and regression models. 
 * BigBraveBN - algorithm for structural learning of Bayesian networks with a large number of nodes. Tested on networks with up to 500 nodes.