diff --git a/gtsam/inference/doc/BayesNet.ipynb b/gtsam/inference/doc/BayesNet.ipynb
index 82f545363..c2b8ff67f 100644
--- a/gtsam/inference/doc/BayesNet.ipynb
+++ b/gtsam/inference/doc/BayesNet.ipynb
@@ -17,7 +17,7 @@
       "source": [
         "A `BayesNet` in GTSAM represents a directed graphical model, specifically the result of running sequential variable elimination (like Cholesky or QR factorization) on a `FactorGraph`.\n",
         "\n",
-        "It is essentially a collection of `Conditional` objects, ordered according to the elimination order. Each conditional represents $P(\text{variable} | \text{parents})$, where the parents are variables that appear later in the elimination ordering.\n",
+        "It is essentially a collection of `Conditional` objects, ordered according to the elimination order. Each conditional represents $P(\\text{variable} | \\text{parents})$, where the parents are variables that appear later in the elimination ordering.\n",
         "\n",
         "A Bayes net represents the joint probability distribution as a product of conditional probabilities stored in the net:\n",
         "\n",
diff --git a/gtsam/inference/doc/BayesTree.ipynb b/gtsam/inference/doc/BayesTree.ipynb
index a5f6479e4..50863543f 100644
--- a/gtsam/inference/doc/BayesTree.ipynb
+++ b/gtsam/inference/doc/BayesTree.ipynb
@@ -15,7 +15,7 @@
         "id": "bayestree_desc_md"
       },
       "source": [
-        "A `BayesTree` is a graphical model that represents the result of multifrontal variable elimination on a `FactorGraph`. It is a tree structure where each node is a 'clique' containing a set of conditional distributions $P(\text{Frontals} | \text{Separator})$.\n",
+        "A `BayesTree` is a graphical model that represents the result of multifrontal variable elimination on a `FactorGraph`. It is a tree structure where each node is a 'clique' containing a set of conditional distributions $P(\\text{Frontals} | \\text{Separator})$.\n",
         "\n",
         "Each clique k contains a conditional $P(F_k∣S_k)$, where $F_k$ are frontal variables and $S_k$ are separator variables. The joint probability distribution encoded by the Bayes tree is given by the product of all clique conditionals:\n",
         "\n",
diff --git a/gtsam/inference/doc/JunctionTree.ipynb b/gtsam/inference/doc/JunctionTree.ipynb
index 7ebafb831..35b326dda 100644
--- a/gtsam/inference/doc/JunctionTree.ipynb
+++ b/gtsam/inference/doc/JunctionTree.ipynb
@@ -19,7 +19,7 @@
         "\n",
         "Key differences from related structures:\n",
         "*   **vs. EliminationTree:** Junction tree nodes can represent the elimination of multiple variables simultaneously (a 'frontal' set), whereas elimination tree nodes typically represent single variable eliminations.\n",
-        "*   **vs. BayesTree:** A JunctionTree node contains the original factors associated with the variables being eliminated in that clique. A BayesTree node contains the *result* of eliminating those factors (i.e., a conditional density $P(\text{Frontals} | \text{Separator})$).\n",
+        "*   **vs. BayesTree:** A JunctionTree node contains the original factors associated with the variables being eliminated in that clique. A BayesTree node contains the *result* of eliminating those factors (i.e., a conditional density $P(\\text{Frontals} | \\text{Separator})$).\n",
         "\n",
         "Like `EliminationTree`, direct manipulation of `JunctionTree` objects in Python is uncommon. It's primarily an internal structure used by `eliminateMultifrontal` when producing a `BayesTree`."
       ]