diff --git a/examples/tutorials/fitting_with_x_and_y_errors.ipynb b/examples/tutorials/fitting_with_x_and_y_errors.ipynb
index 6cda25778ac0c0ec54c1b0dd43677e1d94cd72de..56d2969053d44698519b5737f86d1c039ccf19ad 100644
--- a/examples/tutorials/fitting_with_x_and_y_errors.ipynb
+++ b/examples/tutorials/fitting_with_x_and_y_errors.ipynb
@@ -6,20 +6,7 @@
    "source": [
     "# Fitting a model to data with both x and y errors with `Bilby`\n",
     "\n",
-    "Usually when we fit a model to data with a Gaussian Likelihood we assume that we know x values exactly. This is almost never the case. Here we show how to fit a model with errors in both x and y.\n",
-    "\n",
-    "Since we are using a very simple model we will use the `nestle` sampler.\n",
-    "This can be installed using\n",
-    "\n",
-    "```console\n",
-    "$ conda install -c conda-forge nestle\n",
-    "```\n",
-    "\n",
-    "or\n",
-    "\n",
-    "```console\n",
-    "$ pip install nestle\n",
-    "```"
+    "Usually when we fit a model to data with a Gaussian Likelihood we assume that we know x values exactly. This is almost never the case. Here we show how to fit a model with errors in both x and y."
    ]
   },
   {
@@ -62,10 +49,10 @@
     "    xtrue = np.linspace(0, 100, points)\n",
     "    ytrue = model(x=xtrue, m=m, c=c)\n",
     "\n",
-    "    xerr = xerr * np.random.randn(points)\n",
-    "    yerr = yerr * np.random.randn(points)\n",
-    "    xobs = xtrue + xerr\n",
-    "    yobs = ytrue + yerr\n",
+    "    xerr_vals = xerr * np.random.randn(points)\n",
+    "    yerr_vals = yerr * np.random.randn(points)\n",
+    "    xobs = xtrue + xerr_vals\n",
+    "    yobs = ytrue + yerr_vals\n",
     "\n",
     "    plt.errorbar(xobs, yobs, xerr=xerr, yerr=yerr, fmt=\"x\")\n",
     "    plt.errorbar(xtrue, ytrue, yerr=yerr, color=\"black\", alpha=0.5)\n",
@@ -108,7 +95,7 @@
     "    m=bilby.core.prior.Uniform(0, 30, \"m\"), c=bilby.core.prior.Uniform(0, 30, \"c\")\n",
     ")\n",
     "\n",
-    "sampler_kwargs = dict(priors=priors, sampler=\"nestle\", nlive=1000, outdir=\"outdir\", verbose=False)"
+    "sampler_kwargs = dict(priors=priors, sampler=\"bilby_mcmc\", nsamples=1000, printdt=5, outdir=\"outdir\", verbose=False, clean=True)"
    ]
   },
   {