diff --git a/gtsam/nonlinear/Marginals.cpp b/gtsam/nonlinear/Marginals.cpp
index 11e123f13..7f554b5e5 100644
--- a/gtsam/nonlinear/Marginals.cpp
+++ b/gtsam/nonlinear/Marginals.cpp
@@ -27,7 +27,7 @@ namespace gtsam {
 
 /* ************************************************************************* */
 Marginals::Marginals(const NonlinearFactorGraph& graph, const Values& solution, Factorization factorization,
-                     EliminateableFactorGraph<GaussianFactorGraph>::OptionalOrdering ordering)
+                     boost::optional<const Ordering &> ordering)
                      : values_(solution), factorization_(factorization) {
   gttic(MarginalsConstructor);
   graph_ = *graph.linearize(solution);
@@ -36,12 +36,12 @@ Marginals::Marginals(const NonlinearFactorGraph& graph, const Values& solution,
 
 /* ************************************************************************* */
 Marginals::Marginals(const GaussianFactorGraph& graph, const VectorValues& solution, Factorization factorization,
-                     EliminateableFactorGraph<GaussianFactorGraph>::OptionalOrdering ordering)
+                     boost::optional<const Ordering &> ordering)
                      : graph_(graph), factorization_(factorization) {
   gttic(MarginalsConstructor);
   Values vals;
-  for (const VectorValues::KeyValuePair& v: solution) {
-    vals.insert(v.first, v.second);
+  for (const auto& keyValue: solution) {
+    vals.insert(keyValue.first, keyValue.second);
   }
   values_ = vals;
   computeBayesTree(ordering);
@@ -49,16 +49,15 @@ Marginals::Marginals(const GaussianFactorGraph& graph, const VectorValues& solut
 
 /* ************************************************************************* */
 Marginals::Marginals(const GaussianFactorGraph& graph, const Values& solution, Factorization factorization,
-                     EliminateableFactorGraph<GaussianFactorGraph>::OptionalOrdering ordering)
+                     boost::optional<const Ordering &> ordering)
                      : graph_(graph), values_(solution), factorization_(factorization) {
   gttic(MarginalsConstructor);
   computeBayesTree(ordering);
 }
 
 /* ************************************************************************* */
-void Marginals::computeBayesTree(EliminateableFactorGraph<GaussianFactorGraph>::OptionalOrdering ordering) {
+void Marginals::computeBayesTree(boost::optional<const Ordering &> ordering) {
   // Compute BayesTree
-  factorization_ = factorization;
   if(factorization_ == CHOLESKY)
     bayesTree_ = *graph_.eliminateMultifrontal(ordering, EliminatePreferCholesky);
   else if(factorization_ == QR)
@@ -145,7 +144,7 @@ JointMarginal Marginals::jointMarginalInformation(const KeyVector& variables) co
     // Get dimensions from factor graph
     std::vector<size_t> dims;
     dims.reserve(variablesSorted.size());
-    for(Key key: variablesSorted) {
+    for(const auto& key: variablesSorted) {
       dims.push_back(values_.at(key).dim());
     }
 
@@ -162,7 +161,7 @@ VectorValues Marginals::optimize() const {
 void JointMarginal::print(const std::string& s, const KeyFormatter& formatter) const {
   cout << s << "Joint marginal on keys ";
   bool first = true;
-  for(Key key: keys_) {
+  for(const auto& key: keys_) {
     if(!first)
       cout << ", ";
     else
diff --git a/gtsam/nonlinear/Marginals.h b/gtsam/nonlinear/Marginals.h
index 54a290196..4e5921544 100644
--- a/gtsam/nonlinear/Marginals.h
+++ b/gtsam/nonlinear/Marginals.h
@@ -58,7 +58,7 @@ public:
    * @param ordering An optional variable ordering for elimination.
    */
   Marginals(const NonlinearFactorGraph& graph, const Values& solution, Factorization factorization = CHOLESKY,
-            EliminateableFactorGraph<GaussianFactorGraph>::OptionalOrdering ordering = boost::none);
+            boost::optional<const Ordering &> ordering = boost::none);
 
   /** Construct a marginals class from a linear factor graph.
    * @param graph The factor graph defining the full joint density on all variables.
@@ -67,7 +67,7 @@ public:
    * @param ordering An optional variable ordering for elimination.
    */          
   Marginals(const GaussianFactorGraph& graph, const Values& solution, Factorization factorization = CHOLESKY,
-              EliminateableFactorGraph<GaussianFactorGraph>::OptionalOrdering ordering = boost::none);
+              boost::optional<const Ordering &> ordering = boost::none);
 
   /** Construct a marginals class from a linear factor graph.
    * @param graph The factor graph defining the full joint density on all variables.
@@ -76,7 +76,7 @@ public:
    * @param ordering An optional variable ordering for elimination.
    */          
   Marginals(const GaussianFactorGraph& graph, const VectorValues& solution, Factorization factorization = CHOLESKY,
-              EliminateableFactorGraph<GaussianFactorGraph>::OptionalOrdering ordering = boost::none);
+              boost::optional<const Ordering &> ordering = boost::none);
 
   /** print */
   void print(const std::string& str = "Marginals: ", const KeyFormatter& keyFormatter = DefaultKeyFormatter) const;
@@ -103,7 +103,7 @@ public:
 protected:
   
   /** Compute the Bayes Tree as a helper function to the constructor */
-  void computeBayesTree(EliminateableFactorGraph<GaussianFactorGraph>::OptionalOrdering ordering);
+  void computeBayesTree(boost::optional<const Ordering &> ordering);
 
 };