DOC: Updated a few URLs

examples/gaussian_processes/GP-MaunaLoa.myst.md

@@ -5,9 +5,9 @@ jupytext:
     format_name: myst
     format_version: 0.13
 kernelspec:
-  display_name: default
+  display_name: Python [conda env:base] *
   language: python
-  name: python3
+  name: conda-base-py
 myst:
   substitutions:
     extra_dependencies: bokeh
@@ -41,8 +41,8 @@ Not much was known about how fossil fuel burning influences the climate in the l
 
 The history behind these measurements and their influence on climatology today and other interesting reading:
 
-- http://scrippsco2.ucsd.edu/history_legacy/early_keeling_curve#
-- https://scripps.ucsd.edu/programs/keelingcurve/2016/05/23/why-has-a-drop-in-global-co2-emissions-not-caused-co2-levels-in-the-atmosphere-to-stabilize/#more-1412
+- [History & Legacy: The Early Keeling Curve](http://scrippsco2.ucsd.edu/history_legacy/early_keeling_curve)
+- [Why Has a Drop in Global CO2 Emissions Not Caused CO2 Levels in the Atmosphere to Stabilize?](https://keelingcurve.ucsd.edu/2016/05/23/why-has-a-drop-in-global-co2-emissions-not-caused-co2-levels-in-the-atmosphere-to-stabilize/)
 
 Let's load in the data, tidy it up, and have a look.  The [raw data set is located here](http://scrippsco2.ucsd.edu/data/atmospheric_co2/mlo).  This notebook uses the [Bokeh package](http://bokeh.pydata.org/en/latest/) for plots that benefit from interactivity.
 

examples/references.bib

@@ -39,7 +39,7 @@ @article{bali2003gev
   pages         = {423--427},
   year          = {2003},
   issn          = {0165-1765},
-  doi           = {https://doi.org/10.1016/S0165-1765(03)00035-1},
+  doi           = {10.1016/S0165-1765(03)00035-1},
   url           = {https://www.sciencedirect.com/science/article/pii/S0165176503000351},
   author        = {Turan G. Bali}
 }
@@ -118,7 +118,7 @@ @inproceedings{caprani2009gev
   editor        = "Hitoshi Furuta and Frangopol, {Dan M} and Masanobu Shinozuka",
   booktitle     = "Proceedings of the 10th International Conference on Structural Safety and Reliability (ICOSSAR2009)",
   publisher     = "CRC Press",
-  url           = {https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.722.6789\&rep=rep1\&type=pdf}
+  url           = {https://www.colincaprani.com/files/papers/Conferences/ICOSSAR%2009%20-%20Caprani%20&%20OBrien.pdf}
 }
 @article{caprani2010gev,
   title         = {The use of predictive likelihood to estimate the distribution of extreme bridge traffic load effect},
@@ -128,7 +128,7 @@ @article{caprani2010gev
   pages         = {138--144},
   year          = {2010},
   issn          = {0167-4730},
-  doi           = {https://doi.org/10.1016/j.strusafe.2009.09.001},
+  doi           = {10.1016/j.strusafe.2009.09.001},
   url           = {https://www.sciencedirect.com/science/article/pii/S016747300900071X},
   author        = {Colin C. Caprani and Eugene J. OBrien}
 }

examples/statistical_rethinking_lectures/02-The_Garden_of_Forking_Data.ipynb

@@ -920,7 +920,7 @@
     "plt.bar(ppd_unique, ppd_counts, width=0.2, color=\"C1\", label=\"posterior predictive\")\n",
     "plt.xlabel(r\"$\\hat n_W$\")\n",
     "plt.ylabel(\"count\")\n",
-    "plt.title(f\"number of W samples predicted $\\hat n_W$ from {N_draws_for_prediction} globe flips\")\n",
+    "plt.title(rf\"number of W samples predicted $\\hat n_W$ from {N_draws_for_prediction} globe flips\")\n",
     "plt.legend();"
    ]
   },

examples/statistical_rethinking_lectures/02-The_Garden_of_Forking_Data.myst.md

@@ -471,7 +471,7 @@ plt.bar(
 plt.bar(ppd_unique, ppd_counts, width=0.2, color="C1", label="posterior predictive")
 plt.xlabel(r"$\hat n_W$")
 plt.ylabel("count")
-plt.title(f"number of W samples predicted $\hat n_W$ from {N_draws_for_prediction} globe flips")
+plt.title(rf"number of W samples predicted $\hat n_W$ from {N_draws_for_prediction} globe flips")
 plt.legend();
 ```
 

examples/statistical_rethinking_lectures/04-Categories_&_Curves.ipynb

@@ -3119,7 +3119,7 @@
     "        fill_kwargs=dict(alpha=1),\n",
     "    )\n",
     "    terms = \"+\".join([f\"\\\\beta_{o} H_i^{o}\" for o in range(1, order + 1)])\n",
-    "    plt.title(f\"$\\mu_i = \\\\alpha + {terms}$\")\n",
+    "    plt.title(f\"$\\\\mu_i = \\\\alpha + {terms}$\")\n",
     "\n",
     "\n",
     "for order in [2, 4, 6]:\n",

examples/statistical_rethinking_lectures/04-Categories_&_Curves.myst.md

@@ -904,7 +904,7 @@ def plot_polynomial_model_posterior_predictive(model, inference, data, order):
         fill_kwargs=dict(alpha=1),
     )
     terms = "+".join([f"\\beta_{o} H_i^{o}" for o in range(1, order + 1)])
-    plt.title(f"$\mu_i = \\alpha + {terms}$")
+    plt.title(f"$\\mu_i = \\alpha + {terms}$")
 
 
 for order in [2, 4, 6]:

examples/statistical_rethinking_lectures/05-Elemental_Confounds.ipynb

@@ -1954,7 +1954,7 @@
     "plt.title(\n",
     "    \"Causal effect of increasing Marriage Rate\\n\"\n",
     "    f\"by {n_std_increase} standard deviations on Divorce Rate\\n\"\n",
-    "    f\"$p(\\delta < 0) = {prob_lt_zero:1.2}$\"\n",
+    "    rf\"$p(\\delta < 0) = {prob_lt_zero:1.2}$\"\n",
     ");"
    ]
   },

examples/statistical_rethinking_lectures/05-Elemental_Confounds.myst.md

@@ -674,7 +674,7 @@ plt.axvline(0, color="k", linestyle="--")
 plt.title(
     "Causal effect of increasing Marriage Rate\n"
     f"by {n_std_increase} standard deviations on Divorce Rate\n"
-    f"$p(\delta < 0) = {prob_lt_zero:1.2}$"
+    rf"$p(\delta < 0) = {prob_lt_zero:1.2}$"
 );
 ```
 

examples/statistical_rethinking_lectures/09-Modeling_Events.ipynb

@@ -1305,7 +1305,7 @@
     "    az.plot_dist(ps, color=\"C0\")\n",
     "    plt.xlim([0, 1])\n",
     "    plt.ylabel(\"density\")\n",
-    "    plt.title(f\"$\\\\alpha \\sim \\mathcal{{N}}(0, {std})$\")\n",
+    "    plt.title(f\"$\\\\alpha \\\\sim \\\\mathcal{{N}}(0, {std})$\")\n",
     "    if ii == 2:\n",
     "        plt.xlabel(\"p(event)\")"
    ]

examples/statistical_rethinking_lectures/09-Modeling_Events.myst.md

@@ -493,7 +493,7 @@ for ii, std in enumerate([10, 1.5, 1]):
     az.plot_dist(ps, color="C0")
     plt.xlim([0, 1])
     plt.ylabel("density")
-    plt.title(f"$\\alpha \sim \mathcal{{N}}(0, {std})$")
+    plt.title(f"$\\alpha \\sim \\mathcal{{N}}(0, {std})$")
     if ii == 2:
         plt.xlabel("p(event)")
 ```

examples/statistical_rethinking_lectures/10-Counts_&_Hidden_Confounds.ipynb

@@ -2511,7 +2511,7 @@
     "    # since log(lambda) = alpha, if\n",
     "    # alpha is Normally distributed, therefore lambda is log-normal\n",
     "    lambda_prior_dist = stats.lognorm(s=prior_sigma, scale=np.exp(prior_mu))\n",
-    "    label = f\"$\\\\alpha \\sim \\mathcal{{N}}{prior_mu, prior_sigma}$\"\n",
+    "    label = f\"$\\\\alpha \\\\sim \\\\mathcal{{N}}{prior_mu, prior_sigma}$\"\n",
     "    pdf = lambda_prior_dist.pdf(num_tools)\n",
     "    plt.plot(num_tools, pdf, label=label, linewidth=3)\n",
     "\n",
@@ -2534,7 +2534,9 @@
     "    for sample_idx, lambda_ in enumerate(lambdas):\n",
     "        pmf = stats.poisson(lambda_).pmf(num_tools)\n",
     "\n",
-    "        label = f\"$\\\\alpha \\sim \\mathcal{{N}}{prior_mu, prior_sigma}$\" if sample_idx == 1 else None\n",
+    "        label = (\n",
+    "            f\"$\\\\alpha \\\\sim \\\\mathcal{{N}}{prior_mu, prior_sigma}$\" if sample_idx == 1 else None\n",
+    "        )\n",
     "        color = f\"C{ii}\"\n",
     "        plt.plot(num_tools, pmf, color=color, label=label, alpha=0.1)\n",
     "\n",

examples/statistical_rethinking_lectures/10-Counts_&_Hidden_Confounds.myst.md

@@ -558,7 +558,7 @@ for prior_mu, prior_sigma in normal_alpha_prior_params:
     # since log(lambda) = alpha, if
     # alpha is Normally distributed, therefore lambda is log-normal
     lambda_prior_dist = stats.lognorm(s=prior_sigma, scale=np.exp(prior_mu))
-    label = f"$\\alpha \sim \mathcal{{N}}{prior_mu, prior_sigma}$"
+    label = f"$\\alpha \\sim \\mathcal{{N}}{prior_mu, prior_sigma}$"
     pdf = lambda_prior_dist.pdf(num_tools)
     plt.plot(num_tools, pdf, label=label, linewidth=3)
 
@@ -581,7 +581,9 @@ for ii, (prior_mu, prior_sigma) in enumerate(normal_alpha_prior_params):
     for sample_idx, lambda_ in enumerate(lambdas):
         pmf = stats.poisson(lambda_).pmf(num_tools)
 
-        label = f"$\\alpha \sim \mathcal{{N}}{prior_mu, prior_sigma}$" if sample_idx == 1 else None
+        label = (
+            f"$\\alpha \\sim \\mathcal{{N}}{prior_mu, prior_sigma}$" if sample_idx == 1 else None
+        )
         color = f"C{ii}"
         plt.plot(num_tools, pmf, color=color, label=label, alpha=0.1)
 

examples/statistical_rethinking_lectures/14-Correlated_Features.ipynb

@@ -1545,7 +1545,7 @@
     "    utils.plot_2d_function(xs, ys, prior, colors=\"gray\", ax=axs[ii])\n",
     "    plt.xlabel(\"x\")\n",
     "    plt.ylabel(\"v\")\n",
-    "    plt.title(f\"$v \\sim Normal(0, {sigma_v})$\\n# Divergences: {n_divergences}\")"
+    "    plt.title(f\"$v \\\\sim Normal(0, {sigma_v})$\\n# Divergences: {n_divergences}\")"
    ]
   },
   {
@@ -1722,7 +1722,7 @@
     "    utils.plot_2d_function(xs, ys, prior, colors=\"gray\", ax=axs[ii])\n",
     "    plt.xlabel(\"z\")\n",
     "    plt.ylabel(\"v\")\n",
-    "    plt.title(f\"$v \\sim$ Normal(0, {sigma_v})\\n# Divergences: {n_divergences}\")"
+    "    plt.title(f\"$v \\\\sim$ Normal(0, {sigma_v})\\n# Divergences: {n_divergences}\")"
    ]
   },
   {

examples/statistical_rethinking_lectures/14-Correlated_Features.myst.md

@@ -708,7 +708,7 @@ for ii, sigma_v in enumerate(sigma_vs):
     utils.plot_2d_function(xs, ys, prior, colors="gray", ax=axs[ii])
     plt.xlabel("x")
     plt.ylabel("v")
-    plt.title(f"$v \sim Normal(0, {sigma_v})$\n# Divergences: {n_divergences}")
+    plt.title(f"$v \\sim Normal(0, {sigma_v})$\n# Divergences: {n_divergences}")
 ```
 
 ## What to do?
@@ -754,7 +754,7 @@ for ii, sigma_v in enumerate(sigma_vs):
     utils.plot_2d_function(xs, ys, prior, colors="gray", ax=axs[ii])
     plt.xlabel("z")
     plt.ylabel("v")
-    plt.title(f"$v \sim$ Normal(0, {sigma_v})\n# Divergences: {n_divergences}")
+    plt.title(f"$v \\sim$ Normal(0, {sigma_v})\n# Divergences: {n_divergences}")
 ```
 
 - By reparameterizing, we get to sample multi-dimensional Normal distributions, which are smoother parabolas in the log space.

examples/survival_analysis/censored_data.ipynb

@@ -47,7 +47,7 @@
    "metadata": {},
    "source": [
     "[This example notebook on Bayesian survival\n",
-    "analysis](http://docs.pymc.io/notebooks/survival_analysis.html) touches on the\n",
+    "analysis](https://www.pymc.io/projects/examples/en/latest/survival_analysis/survival_analysis.html) touches on the\n",
     "point of censored data. _Censoring_ is a form of missing-data problem, in which\n",
     "observations greater than a certain threshold are clipped down to that\n",
     "threshold, or observations less than a certain threshold are clipped up to that\n",
@@ -726,9 +726,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pymc",
+   "display_name": "Python [conda env:base] *",
    "language": "python",
-   "name": "python3"
+   "name": "conda-base-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -740,7 +740,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0 | packaged by conda-forge | (main, Oct 25 2022, 06:24:40) [GCC 10.4.0]"
+   "version": "3.11.5"
   },
   "vscode": {
    "interpreter": {

examples/survival_analysis/censored_data.myst.md

@@ -5,9 +5,9 @@ jupytext:
     format_name: myst
     format_version: 0.13
 kernelspec:
-  display_name: pymc
+  display_name: Python [conda env:base] *
   language: python
-  name: python3
+  name: conda-base-py
 ---
 
 (censored_data)=
@@ -38,7 +38,7 @@ az.style.use("arviz-darkgrid")
 ```
 
 [This example notebook on Bayesian survival
-analysis](http://docs.pymc.io/notebooks/survival_analysis.html) touches on the
+analysis](https://www.pymc.io/projects/examples/en/latest/survival_analysis/survival_analysis.html) touches on the
 point of censored data. _Censoring_ is a form of missing-data problem, in which
 observations greater than a certain threshold are clipped down to that
 threshold, or observations less than a certain threshold are clipped up to that