diff --git a/examples/PlanarSLAMSelfContained_advanced.cpp b/examples/PlanarSLAMSelfContained_advanced.cpp
index 70a26e261..2928139a6 100644
--- a/examples/PlanarSLAMSelfContained_advanced.cpp
+++ b/examples/PlanarSLAMSelfContained_advanced.cpp
@@ -122,7 +122,7 @@ int main(int argc, char** argv) {
 
 	initial.print("initial estimate");
 
-	// optimize using Levenburg-Marquadt optimization with an ordering from colamd
+	// optimize using Levenberg-Marquardt optimization with an ordering from colamd
 
 	// first using sequential elimination
 	OptimizerSeqential::shared_values resultSequential = OptimizerSeqential::optimizeLM(graph, initial);
diff --git a/examples/Pose2SLAMExample_advanced.cpp b/examples/Pose2SLAMExample_advanced.cpp
index 26d35d7fa..f62d2f4cf 100644
--- a/examples/Pose2SLAMExample_advanced.cpp
+++ b/examples/Pose2SLAMExample_advanced.cpp
@@ -76,11 +76,12 @@ int main(int argc, char** argv) {
 	Values result = *optimizer_result.values();
 	result.print("final result");
 
-	Vector mean; Matrix covariance;
-	boost::tie( mean, covariance)  = optimizer_result.marginalStandard(x1);
+	/* Get covariances */
+	Matrix covariance1  = optimizer_result.marginalStandard(x1);
+	Matrix covariance2  = optimizer_result.marginalStandard(x2);
 
-	print(mean, "Mean");
-	print(covariance, "Covariance");
+	print(covariance1, "Covariance1");
+	print(covariance2, "Covariance2");
 
 	return 0;
 }
diff --git a/nonlinear/NonlinearOptimizer.h b/nonlinear/NonlinearOptimizer.h
index de2e0618c..58bd62c57 100644
--- a/nonlinear/NonlinearOptimizer.h
+++ b/nonlinear/NonlinearOptimizer.h
@@ -161,8 +161,8 @@ namespace gtsam {
 		/**
 		 * Return mean and covariance on a single variable
 		 */
-		std::pair<Vector, Matrix> marginalStandard(Symbol j) const {
-			return solver_->marginalStandard((*ordering_)[j]);
+		Matrix marginalStandard(Symbol j) const {
+			return solver_->marginalStandard((*ordering_)[j]).second;
 		}
 
 		/**