From 59e5794e5d6f3220e6ee99cdf351e4c9ea5c3b66 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ricardo=20Monta=C3=B1ana?= <rmontanana@gmail.com>
Date: Sun, 2 Jul 2023 11:39:12 +0200
Subject: [PATCH] Update info on README

---
 README.md | 38 +++++++++++++++++++++++++++++++++++++-
 1 file changed, 37 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 5d210c5..4fd0db2 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,39 @@
 # BayesNet
 
-Trying to implement Bayesian Network inference with libtorch from scratch
+Bayesian Network Classifier with libtorch from scratch
+
+## Variable Elimination
+
+To decide the first variable to eliminate wel use the MinFill criterion, that is
+the variable that minimizes the number of edges that need to be added to the
+graph to make it triangulated.
+This is done by counting the number of edges that need to be added to the graph
+if the variable is eliminated. The variable with the minimum number of edges is
+chosen.
+In pgmpy this is done computing then the length of the combinations of the
+neighbors taken 2 by 2.
+
+Once the variable to eliminate is chosen, we need to compute the factors that
+need to be multiplied to get the new factor.
+This is done by multiplying all the factors that contain the variable to
+eliminate and then marginalizing the variable out.
+
+The new factor is then added to the list of factors and the variable to
+eliminate is removed from the list of variables.
+
+The process is repeated until there are no more variables to eliminate.
+
+## Code for combination
+
+// Combinations of length 2
+vector<string> combinations(vector<string> source)
+{
+    vector<string> result;
+    for (int i = 0; i < source.size(); ++i) {
+        string temp = source[i];
+        for (int j = i + 1; j < source.size(); ++j) {
+            result.push_back(temp + source[j]);
+        }
+    }
+    return result;
+}