diff --git a/gtsam/inference/GenericMultifrontalSolver-inl.h b/gtsam/inference/GenericMultifrontalSolver-inl.h
index bd916fbe2..a202a4700 100644
--- a/gtsam/inference/GenericMultifrontalSolver-inl.h
+++ b/gtsam/inference/GenericMultifrontalSolver-inl.h
@@ -23,64 +23,71 @@
 #include <gtsam/inference/JunctionTree-inl.h>
 #include <gtsam/inference/BayesNet-inl.h>
 
-#include <boost/foreach.hpp>
-
 using namespace std;
 
 namespace gtsam {
 
-/* ************************************************************************* */
-template<class FACTOR, class JUNCTIONTREE>
-GenericMultifrontalSolver<FACTOR, JUNCTIONTREE>::GenericMultifrontalSolver(const FactorGraph<FACTOR>& factorGraph) :
-    structure_(new VariableIndex(factorGraph)), junctionTree_(new JUNCTIONTREE(factorGraph, *structure_)) {}
+	/* ************************************************************************* */
+	template<class F, class JT>
+	GenericMultifrontalSolver<F, JT>::GenericMultifrontalSolver(
+			const FactorGraph<F>& graph) :
+			structure_(new VariableIndex(graph)), junctionTree_(
+					new JT(graph, *structure_)) {
+	}
 
-/* ************************************************************************* */
-template<class FACTOR, class JUNCTIONTREE>
-GenericMultifrontalSolver<FACTOR, JUNCTIONTREE>::GenericMultifrontalSolver(
-    const typename FactorGraph<FACTOR>::shared_ptr& factorGraph, const VariableIndex::shared_ptr& variableIndex) :
-    structure_(variableIndex), junctionTree_(new JUNCTIONTREE(*factorGraph, *structure_)) {}
+	/* ************************************************************************* */
+	template<class F, class JT>
+	GenericMultifrontalSolver<F, JT>::GenericMultifrontalSolver(
+			const typename FactorGraph<F>::shared_ptr& graph,
+			const VariableIndex::shared_ptr& variableIndex) :
+			structure_(variableIndex), junctionTree_(new JT(*graph, *structure_)) {
+	}
 
-/* ************************************************************************* */
-template<class FACTOR, class JUNCTIONTREE>
-void GenericMultifrontalSolver<FACTOR, JUNCTIONTREE>::replaceFactors(const typename FactorGraph<FACTOR>::shared_ptr& factorGraph) {
-  junctionTree_.reset(new JUNCTIONTREE(*factorGraph, *structure_));
-}
+	/* ************************************************************************* */
+	template<class F, class JT>
+	void GenericMultifrontalSolver<F, JT>::replaceFactors(
+			const typename FactorGraph<F>::shared_ptr& graph) {
+		junctionTree_.reset(new JT(*graph, *structure_));
+	}
 
-/* ************************************************************************* */
-template<class FACTOR, class JUNCTIONTREE>
-typename JUNCTIONTREE::BayesTree::shared_ptr GenericMultifrontalSolver<
-		FACTOR, JUNCTIONTREE>::eliminate(
-		typename FactorGraph<FACTOR>::Eliminate function) const {
-	typename JUNCTIONTREE::BayesTree::shared_ptr bayesTree(
-			new typename JUNCTIONTREE::BayesTree);
-	bayesTree->insert(junctionTree_->eliminate(function));
-	return bayesTree;
-}
+	/* ************************************************************************* */
+	template<class F, class JT>
+	typename JT::BayesTree::shared_ptr GenericMultifrontalSolver<F, JT>::eliminate(
+			typename FactorGraph<F>::Eliminate function) const {
 
-/* ************************************************************************* */
-template<class FACTOR, class JUNCTIONTREE>
-typename FactorGraph<FACTOR>::shared_ptr GenericMultifrontalSolver<FACTOR,
-		JUNCTIONTREE>::jointFactorGraph(const std::vector<Index>& js,
-		Eliminate function) const {
+		// eliminate junction tree, returns pointer to root
+		typename JT::BayesTree::sharedClique root = junctionTree_->eliminate(function);
 
-	// We currently have code written only for computing the
+		// create an empty Bayes tree and insert root clique
+		typename JT::BayesTree::shared_ptr bayesTree(new typename JT::BayesTree);
+		bayesTree->insert(root);
 
-	if (js.size() != 2) throw domain_error(
-			"*MultifrontalSolver::joint(js) currently can only compute joint marginals\n"
-				"for exactly two variables.  You can call marginal to compute the\n"
-				"marginal for one variable.  *SequentialSolver::joint(js) can compute the\n"
-				"joint marginal over any number of variables, so use that if necessary.\n");
+		// return the Bayes tree
+		return bayesTree;
+	}
 
-	return eliminate(function)->joint(js[0], js[1], function);
-}
+	/* ************************************************************************* */
+	template<class F, class JT>
+	typename FactorGraph<F>::shared_ptr GenericMultifrontalSolver<F, JT>::jointFactorGraph(
+			const std::vector<Index>& js, Eliminate function) const {
 
-/* ************************************************************************* */
-template<class FACTOR, class JUNCTIONTREE>
-typename FACTOR::shared_ptr GenericMultifrontalSolver<FACTOR, JUNCTIONTREE>::marginalFactor(
-		Index j, Eliminate function) const {
-	return eliminate(function)->marginalFactor(j, function);
-}
+		// We currently have code written only for computing the
+
+		if (js.size() != 2) throw domain_error(
+				"*MultifrontalSolver::joint(js) currently can only compute joint marginals\n"
+						"for exactly two variables.  You can call marginal to compute the\n"
+						"marginal for one variable.  *SequentialSolver::joint(js) can compute the\n"
+						"joint marginal over any number of variables, so use that if necessary.\n");
+
+		return eliminate(function)->joint(js[0], js[1], function);
+	}
+
+	/* ************************************************************************* */
+	template<class F, class JT>
+	typename F::shared_ptr GenericMultifrontalSolver<F, JT>::marginalFactor(
+			Index j, Eliminate function) const {
+		return eliminate(function)->marginalFactor(j, function);
+	}
 
 }
 
-