diff --git a/gtsam/linear/GaussianConditional.cpp b/gtsam/linear/GaussianConditional.cpp
index 5e8a193cf..7cdff914f 100644
--- a/gtsam/linear/GaussianConditional.cpp
+++ b/gtsam/linear/GaussianConditional.cpp
@@ -63,13 +63,24 @@ namespace gtsam {
       : BaseFactor(key, R, parent1, S, parent2, T, d, sigmas),
         BaseConditional(1) {}
 
+  /* ************************************************************************ */
+  GaussianConditional GaussianConditional::FromMeanAndStddev(Key key,
+                                                             const Vector& mu,
+                                                             double sigma) {
+    // |Rx - d| = |x-(Ay + b)|/sigma
+    const Matrix R = Matrix::Identity(mu.size(), mu.size());
+    const Vector& d = mu;
+    return GaussianConditional(key, d, R,
+                               noiseModel::Isotropic::Sigma(mu.size(), sigma));
+  }
+
   /* ************************************************************************ */
   GaussianConditional GaussianConditional::FromMeanAndStddev(
       Key key, const Matrix& A, Key parent, const Vector& b, double sigma) {
     // |Rx + Sy - d| = |x-(Ay + b)|/sigma
     const Matrix R = Matrix::Identity(b.size(), b.size());
     const Matrix S = -A;
-    const Vector d = b;
+    const Vector& d = b;
     return GaussianConditional(key, d, R, parent, S,
                                noiseModel::Isotropic::Sigma(b.size(), sigma));
   }
@@ -82,7 +93,7 @@ namespace gtsam {
     const Matrix R = Matrix::Identity(b.size(), b.size());
     const Matrix S = -A1;
     const Matrix T = -A2;
-    const Vector d = b;
+    const Vector& d = b;
     return GaussianConditional(key, d, R, parent1, S, parent2, T,
                                noiseModel::Isotropic::Sigma(b.size(), sigma));
   }
diff --git a/gtsam/linear/GaussianConditional.h b/gtsam/linear/GaussianConditional.h
index a72a73973..af1c5d80e 100644
--- a/gtsam/linear/GaussianConditional.h
+++ b/gtsam/linear/GaussianConditional.h
@@ -84,12 +84,17 @@ namespace gtsam {
       const KEYS& keys, size_t nrFrontals, const VerticalBlockMatrix& augmentedMatrix,
       const SharedDiagonal& sigmas = SharedDiagonal());
 
-    /// Construct from mean A1 p1 + b and standard deviation.
+    /// Construct from mean `mu` and standard deviation `sigma`.
+    static GaussianConditional FromMeanAndStddev(Key key, const Vector& mu,
+                                                 double sigma);
+
+    /// Construct from conditional mean `A1 p1 + b` and standard deviation.
     static GaussianConditional FromMeanAndStddev(Key key, const Matrix& A,
                                                  Key parent, const Vector& b,
                                                  double sigma);
 
-    /// Construct from mean A1 p1 + A2 p2 + b and standard deviation.
+    /// Construct from conditional mean `A1 p1 + A2 p2 + b` and standard
+    /// deviation `sigma`.
     static GaussianConditional FromMeanAndStddev(Key key,  //
                                                  const Matrix& A1, Key parent1,
                                                  const Matrix& A2, Key parent2,
diff --git a/gtsam/linear/linear.i b/gtsam/linear/linear.i
index f5857b0c5..6f241da55 100644
--- a/gtsam/linear/linear.i
+++ b/gtsam/linear/linear.i
@@ -470,6 +470,10 @@ virtual class GaussianConditional : gtsam::JacobianFactor {
                       size_t name2, Matrix T);
 
   // Named constructors
+  static gtsam::GaussianConditional FromMeanAndStddev(gtsam::Key key, 
+                                                      const Vector& mu,
+                                                      double sigma);
+
   static gtsam::GaussianConditional FromMeanAndStddev(gtsam::Key key, 
                                                       const Matrix& A,
                                                       gtsam::Key parent,