diff --git a/src/BayesNet/Network.cc b/src/BayesNet/Network.cc
index da8898c..b9fc659 100644
--- a/src/BayesNet/Network.cc
+++ b/src/BayesNet/Network.cc
@@ -133,9 +133,9 @@ namespace bayesnet {
     {
         // Set states to every Node in the network
         for (int i = 0; i < features.size(); ++i) {
-            nodes[features[i]]->setNumStates(states.at(features[i]).size());
+            nodes.at(features.at(i))->setNumStates(states.at(features[i]).size());
         }
-        classNumStates = nodes[className]->getNumStates();
+        classNumStates = nodes.at(className)->getNumStates();
     }
     // X comes in nxm, where n is the number of features and m the number of samples
     void Network::fit(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& weights, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states)
@@ -174,10 +174,16 @@ namespace bayesnet {
     {
         setStates(states);
         laplaceSmoothing = 1.0 / samples.size(1); // To use in CPT computation
+        vector<thread> threads;
         for (auto& node : nodes) {
-            node.second->computeCPT(samples, features, laplaceSmoothing, weights);
-            fitted = true;
+            threads.emplace_back([this, &node, &weights]() {
+                node.second->computeCPT(samples, features, laplaceSmoothing, weights);
+                });
         }
+        for (auto& thread : threads) {
+            thread.join();
+        }
+        fitted = true;
     }
     torch::Tensor Network::predict_tensor(const torch::Tensor& samples, const bool proba)
     {