diff --git a/gtsam/linear/GaussianConditional.cpp b/gtsam/linear/GaussianConditional.cpp
index 7cdff914f..ecfa02282 100644
--- a/gtsam/linear/GaussianConditional.cpp
+++ b/gtsam/linear/GaussianConditional.cpp
@@ -168,26 +168,30 @@ namespace gtsam {
 
 /* ************************************************************************* */
 double GaussianConditional::logDeterminant() const {
-  double logDet;
-  if (this->get_model()) {
-    Vector diag = this->R().diagonal();
-    this->get_model()->whitenInPlace(diag);
-    logDet = diag.unaryExpr([](double x) { return log(x); }).sum();
+  if (get_model()) {
+    Vector diag = R().diagonal();
+    get_model()->whitenInPlace(diag);
+    return diag.unaryExpr([](double x) { return log(x); }).sum();
   } else {
-    logDet =
-        this->R().diagonal().unaryExpr([](double x) { return log(x); }).sum();
+    return R().diagonal().unaryExpr([](double x) { return log(x); }).sum();
   }
-  return logDet;
 }
 
 /* ************************************************************************* */
-//  density = exp(-error(x)) / sqrt((2*pi)^n*det(Sigma))
-//  log = -error(x) - 0.5 * n*log(2*pi) - 0.5 * log det(Sigma)
-double GaussianConditional::logDensity(const VectorValues& x) const {
+//  normalization constant = 1.0 / sqrt((2*pi)^n*det(Sigma))
+//  log = - 0.5 * n*log(2*pi) - 0.5 * log det(Sigma)
+double GaussianConditional::logNormalizationConstant() const {
   constexpr double log2pi = 1.8378770664093454835606594728112;
   size_t n = d().size();
   // log det(Sigma)) = - 2.0 * logDeterminant()
-  return - error(x) - 0.5 * n * log2pi + logDeterminant();
+  return - 0.5 * n * log2pi + logDeterminant();
+}
+
+/* ************************************************************************* */
+//  density = k exp(-error(x))
+//  log = log(k) -error(x) - 0.5 * n*log(2*pi)
+double GaussianConditional::logDensity(const VectorValues& x) const {
+  return logNormalizationConstant() - error(x);
 }
 
 /* ************************************************************************* */
diff --git a/gtsam/linear/GaussianConditional.h b/gtsam/linear/GaussianConditional.h
index af1c5d80e..d25efb2e1 100644
--- a/gtsam/linear/GaussianConditional.h
+++ b/gtsam/linear/GaussianConditional.h
@@ -169,7 +169,7 @@ namespace gtsam {
      *
      * @return double
      */
-    double determinant() const { return exp(this->logDeterminant()); }
+    inline double determinant() const { return exp(logDeterminant()); }
 
     /**
      * @brief Compute the log determinant of the R matrix.
@@ -184,6 +184,19 @@ namespace gtsam {
      */
     double logDeterminant() const;
 
+    /**
+     * normalization constant = 1.0 / sqrt((2*pi)^n*det(Sigma))
+     * log = - 0.5 * n*log(2*pi) - 0.5 * log det(Sigma)
+     */
+    double logNormalizationConstant() const;
+
+    /**
+     * normalization constant = 1.0 / sqrt((2*pi)^n*det(Sigma))
+     */
+    inline double normalizationConstant() const {
+      return exp(logNormalizationConstant());
+    }
+
     /**
     * Solves a conditional Gaussian and writes the solution into the entries of
     * \c x for each frontal variable of the conditional.  The parents are