diff --git a/gtsam/hybrid/HybridBayesNet.h b/gtsam/hybrid/HybridBayesNet.h
index c6ac6dcec..f8ec60911 100644
--- a/gtsam/hybrid/HybridBayesNet.h
+++ b/gtsam/hybrid/HybridBayesNet.h
@@ -145,8 +145,10 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
   AlgebraicDecisionTree<Key> error(const VectorValues &continuousValues) const;
 
   /**
-   * @brief Compute unnormalized probability for each discrete assignment,
-   * and return as a tree.
+   * @brief Compute unnormalized probability q(μ|M),
+   * for each discrete assignment, and return as a tree.
+   * q(μ|M) is the unnormalized probability at the MLE point μ,
+   * conditioned on the discrete variables.
    *
    * @param continuousValues Continuous values at which to compute the
    * probability.
diff --git a/gtsam/hybrid/HybridGaussianFactorGraph.h b/gtsam/hybrid/HybridGaussianFactorGraph.h
index c7e9aa60d..ac9ae1a46 100644
--- a/gtsam/hybrid/HybridGaussianFactorGraph.h
+++ b/gtsam/hybrid/HybridGaussianFactorGraph.h
@@ -99,11 +99,12 @@ class GTSAM_EXPORT HybridGaussianFactorGraph
   using shared_ptr = boost::shared_ptr<This>;  ///< shared_ptr to This
 
   using Values = gtsam::Values;  ///< backwards compatibility
-  using Indices = KeyVector;     ///> map from keys to values
+  using Indices = KeyVector;     ///< map from keys to values
 
   /// @name Constructors
   /// @{
 
+  /// @brief Default constructor.
   HybridGaussianFactorGraph() = default;
 
   /**
@@ -174,14 +175,16 @@ class GTSAM_EXPORT HybridGaussianFactorGraph
    * @brief Compute error for each discrete assignment,
    * and return as a tree.
    *
+   * Error \f$ e = \Vert x - \mu \Vert_{\Sigma} \f$.
+   *
    * @param continuousValues Continuous values at which to compute the error.
    * @return AlgebraicDecisionTree<Key>
    */
   AlgebraicDecisionTree<Key> error(const VectorValues& continuousValues) const;
 
   /**
-   * @brief Compute unnormalized probability for each discrete assignment,
-   * and return as a tree.
+   * @brief Compute unnormalized probability \f$ P(X | M, Z) \f$
+   * for each discrete assignment, and return as a tree.
    *
    * @param continuousValues Continuous values at which to compute the
    * probability.