diff --git a/examples/advanced_examples/Global Parameter Optimisation.ipynb b/examples/advanced_examples/Global Parameter Optimisation.ipynb
index 014084353..b1dae3b5d 100644
--- a/examples/advanced_examples/Global Parameter Optimisation.ipynb	
+++ b/examples/advanced_examples/Global Parameter Optimisation.ipynb	
@@ -13,12 +13,6 @@
    "source": [
     "This notebook demonstrates how to optimize parameters in state space models using external optimization packages, such as [Optim.jl](https://github.com/JuliaNLSolvers/Optim.jl/) and [Flux.jl](https://github.com/FluxML/Flux.jl). We utilize **RxInfer.jl**, a powerful package for inference in probabilistic models.\n",
     "\n",
-    "[Section 1: Univariate State Space Model](#univariate-state-space-model)\n",
-    "\n",
-    "[Section 2: Multivariate State Space Model](#multivariate-state-space-model)\n",
-    "\n",
-    "[Section 3: Learning Kalman Filter with LSTM-Driven Dynamic](#learning-kalman-filter-with-lstm-driven-dynamic)\n",
-    "\n",
     "By the end of this notebook, you will have practical knowledge of global parameter optimization in state space models. You will learn how to optimize parameters in both univariate and multivariate state space models, and harness the power of external optimization packages such as **Optim.jl** and **Flux.jl**."
    ]
   },
@@ -44,7 +38,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Univariate State Space Model<a id='univariate-state-space-model'></a>\n",
+    "## Univariate State Space Model\n",
     "\n",
     "Let us try use the following simple state space model:\n",
     "\n",
@@ -239,7 +233,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Multivariate state space model <a id='multivariate-state-space-model'></a>"
+    "## Multivariate state space model"
    ]
   },
   {
@@ -744,7 +738,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Learning Kalman filter with LSTM driven dynamic<a id='learning-kalman-filter-with-lstm-driven-dynamic'></a>"
+    "## Learning Kalman filter with LSTM driven dynamic"
    ]
   },
   {
diff --git a/examples/basic_examples/Kalman filtering and smoothing.ipynb b/examples/basic_examples/Kalman filtering and smoothing.ipynb
index fe53050e0..1743ce395 100644
--- a/examples/basic_examples/Kalman filtering and smoothing.ipynb	
+++ b/examples/basic_examples/Kalman filtering and smoothing.ipynb	
@@ -54,24 +54,12 @@
     "Utimately, we show how **RxInfer.jl** can deal with missing observations."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "4d3fcfcb-ff7d-4c59-beda-8acd3a20b85d",
-   "metadata": {},
-   "source": [
-    "[Section 1: Gaussian Linear Dynamical System](#gaussian-linear-dynamical-system)\n",
-    "\n",
-    "[Section 2: Multivariate State Space Model](#multivariate-state-space-model)\n",
-    "\n",
-    "[Section 3: Handling Missing Data](#missing_data)\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "aab4197e-4522-493f-8f5d-254140e6c326",
    "metadata": {},
    "source": [
-    "## Gaussian Linear Dynamical System <a id=\"gaussian-linear-dynamical-system\"></a>"
+    "## Gaussian Linear Dynamical System"
    ]
   },
   {
@@ -6614,7 +6602,7 @@
    "id": "d9720084-d4a4-4928-bb3c-569a70a14d38",
    "metadata": {},
    "source": [
-    "### Handling Missing Data<a id='missing_data'></a>"
+    "## Handling Missing Data"
    ]
   },
   {