diff --git a/gtsam/nonlinear/NonlinearFactor.h b/gtsam/nonlinear/NonlinearFactor.h
index 73c150695..a5a838881 100644
--- a/gtsam/nonlinear/NonlinearFactor.h
+++ b/gtsam/nonlinear/NonlinearFactor.h
@@ -118,7 +118,7 @@ public:
 
   /**
    * Calculate the error of the factor
-   * This is typically equal to log-likelihood, e.g. 0.5(h(x)-z)^2/sigma^2 in case of Gaussian.
+   * This is typically equal to log-likelihood, e.g. \f$ 0.5(h(x)-z)^2/sigma^2 \f$ in case of Gaussian.
    * You can override this for systems with unusual noise models.
    */
   virtual double error(const Values& c) const = 0;
diff --git a/gtsam/nonlinear/NonlinearFactorGraph.h b/gtsam/nonlinear/NonlinearFactorGraph.h
index 82b364ddf..429bf0102 100644
--- a/gtsam/nonlinear/NonlinearFactorGraph.h
+++ b/gtsam/nonlinear/NonlinearFactorGraph.h
@@ -49,7 +49,7 @@ namespace gtsam {
     /** return keys in some random order */
     std::set<Key> keys() const;
 
-		/** unnormalized error */
+		/** unnormalized error, \f$ 0.5 \sum_i (h_i(X_i)-z)^2/\sigma^2 \f$ in the most common case */
 		double error(const Values& c) const;
 
 		/** Unnormalized probability. O(n) */