Fix more

Author: zaxtax

examples/howto/marginalizing-models.ipynb

@@ -853,7 +853,7 @@
    "id": "e8dd6e73-6d3b-4ee0-9bff-eb0a581399af",
    "metadata": {},
    "source": [
-    "That's a relatively simple example. The same methods work well-enough even for the [coal mining](https://www.pymc.io/projects/docs/en/stable/learn/core_notebooks/pymc_overview.html#case-study-2-coal-mining-disasters) switchpoint model. The coal mining dataset records the number of coal mining disasters in the UK between 1851 and 1962. The time series dataset captures a time when mining safety regulations are being introduced, we try to estimate when this occurred using a discrete `switchpoint` variable."
+    "That's a relatively simple example. The same methods work well-enough even for the {ref}`Coal mining <pymc:pymc_overview#case-study-2-coal-mining-disasters>` switchpoint model. The coal mining dataset records the number of coal mining disasters in the UK between 1851 and 1962. The time series dataset captures a time when mining safety regulations are being introduced, we try to estimate when this occurred using a discrete `switchpoint` variable."
    ]
   },
   {
@@ -1388,8 +1388,8 @@
    "source": [
     "plt.scatter(years, after_marg.posterior.lp_switchpoint.mean(dim=[\"chain\", \"draw\"]))\n",
     "plt.axvline(x=1891, c=\"orange\")\n",
-    "plt.xlabel(\"$\\mathrm{year}$\")\n",
-    "plt.ylabel(\"$\\log p(\\mathrm{switchpoint}=\\mathrm{year})$\");"
+    "plt.xlabel(r\"$\\mathrm{year}$\")\n",
+    "plt.ylabel(r\"$\\log p(\\mathrm{switchpoint}=\\mathrm{year})$\");"
    ]
   },
   {

examples/howto/marginalizing-models.myst.md

@@ -156,7 +156,7 @@ One important thing to notice is that this discrete variable has a lower ESS, an
 
 +++
 
-That's a relatively simple example. The same methods work well-enough even for the [coal mining](https://www.pymc.io/projects/docs/en/stable/learn/core_notebooks/pymc_overview.html#case-study-2-coal-mining-disasters) switchpoint model. The coal mining dataset records the number of coal mining disasters in the UK between 1851 and 1962. The time series dataset captures a time when mining safety regulations are being introduced, we try to estimate when this occurred using a discrete `switchpoint` variable.
+That's a relatively simple example. The same methods work well-enough even for the {ref}`Coal mining <pymc:pymc_overview#case-study-2-coal-mining-disasters>` switchpoint model. The coal mining dataset records the number of coal mining disasters in the UK between 1851 and 1962. The time series dataset captures a time when mining safety regulations are being introduced, we try to estimate when this occurred using a discrete `switchpoint` variable.
 
 ```{code-cell} ipython3
 # fmt: off
@@ -220,8 +220,8 @@ While `recover_marginals` is able to sample the discrete variables that were mar
 ```{code-cell} ipython3
 plt.scatter(years, after_marg.posterior.lp_switchpoint.mean(dim=["chain", "draw"]))
 plt.axvline(x=1891, c="orange")
-plt.xlabel("$\mathrm{year}$")
-plt.ylabel("$\log p(\mathrm{switchpoint}=\mathrm{year})$");
+plt.xlabel(r"$\mathrm{year}$")
+plt.ylabel(r"$\log p(\mathrm{switchpoint}=\mathrm{year})$");
 ```
 
 ```{code-cell} ipython3