diff --git a/gtsam/nonlinear/doc/PriorFactor.ipynb b/gtsam/nonlinear/doc/PriorFactor.ipynb
index c54f84ce98..2d819c7f05 100644
--- a/gtsam/nonlinear/doc/PriorFactor.ipynb
+++ b/gtsam/nonlinear/doc/PriorFactor.ipynb
@@ -33,7 +33,7 @@
         "\n",
         "## Usage Considerations\n",
         "\n",
-        "- **Noise Model**: The choice of noise model is critical as it determines how strongly the prior is enforced. A tighter noise model implies a stronger belief in the prior. *Note that very strong priors might make the condition number of the linear systems to be solved very high. In this case consider adding a [NonlinearEqualityFactor]\n",
+        "- **Noise Model**: The choice of noise model is critical as it determines how strongly the prior is enforced. A tighter noise model implies a stronger belief in the prior. *Note that very strong priors might make the condition number of the linear systems to be solved very high. In this case consider adding a [NonlinearEqualityFactor](./NonlinearEquality.ipynb)\n",
         "- **Integration with Other Factors**: The `PriorFactor` is typically used in conjunction with other factors that model the system dynamics and measurements. It helps anchor the solution, especially in scenarios with limited or noisy measurements.\n",
         "- **Applications**: Common applications include SLAM (Simultaneous Localization and Mapping), where priors on initial poses or landmarks can significantly improve map accuracy and convergence speed."
       ]