From 2d10192bef9a204d2a7bf76fd3d1478a453130fe Mon Sep 17 00:00:00 2001
From: Josh Cook <joshuacook0023@gmail.com>
Date: Fri, 3 Dec 2021 07:05:47 -0500
Subject: [PATCH] chore: render site

---
 docs/about.html                               |    1 +
 docs/index.html                               |    7 +
 docs/notes/12_extended-topics.Rmd             |   58 +
 docs/notes/12_extended-topics.html            | 2604 +++++++++++++++++
 .../12_extended-topics/slides-extra_s23.jpg   |  Bin 0 -> 186146 bytes
 docs/project-guidelines.html                  |    1 +
 docs/search.json                              |    8 +-
 docs/sitemap.xml                              |    4 +-
 docs/slides/slides_extra.pdf                  |  Bin 0 -> 2559163 bytes
 9 files changed, 2677 insertions(+), 6 deletions(-)
 create mode 100644 docs/notes/12_extended-topics.Rmd
 create mode 100644 docs/notes/12_extended-topics.html
 create mode 100644 docs/notes/assets/12_extended-topics/slides-extra_s23.jpg
 create mode 100644 docs/slides/slides_extra.pdf

diff --git a/docs/about.html b/docs/about.html
index 8ced4e7..f267d24 100644
--- a/docs/about.html
+++ b/docs/about.html
@@ -2395,6 +2395,7 @@ <h3>${suggestion.title}</h3>
 <a href="notes/09_model-selection_bda3-7.html">Section 9. Model comparison and selection</a>
 <a href="notes/10_decision-analysis_bda3-9.html">Section 10. Decision analysis</a>
 <a href="notes/11_normal-approx-freq-properties_bda3-04.html">Section 11. Normal approximation &amp; Frequency properties</a>
+<a href="notes/12_extended-topics.html">Section 12. Extended topics</a>
 </div>
 </div>
 <div class="nav-dropdown">
diff --git a/docs/index.html b/docs/index.html
index b4e3f3e..6b08346 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -2396,6 +2396,7 @@ <h3>${suggestion.title}</h3>
 <a href="notes/09_model-selection_bda3-7.html">Section 9. Model comparison and selection</a>
 <a href="notes/10_decision-analysis_bda3-9.html">Section 10. Decision analysis</a>
 <a href="notes/11_normal-approx-freq-properties_bda3-04.html">Section 11. Normal approximation &amp; Frequency properties</a>
+<a href="notes/12_extended-topics.html">Section 12. Extended topics</a>
 </div>
 </div>
 <div class="nav-dropdown">
@@ -2556,6 +2557,12 @@ <h2 id="sections">Sections</h2>
 <td>(none)</td>
 <td>(none)</td>
 </tr>
+<tr class="even">
+<td><strong>12. Extended topics</strong></td>
+<td><a href="notes/12_extended-topics.html">notes</a></td>
+<td>(none)</td>
+<td>(none)</td>
+</tr>
 </tbody>
 </table>
 <h2 id="stan-models">Stan models</h2>
diff --git a/docs/notes/12_extended-topics.Rmd b/docs/notes/12_extended-topics.Rmd
new file mode 100644
index 0000000..aa10b6c
--- /dev/null
+++ b/docs/notes/12_extended-topics.Rmd
@@ -0,0 +1,58 @@
+---
+title: "12. Extended topics"
+date: "2021-12-02"
+output: distill::distill_article
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE, dpi = 300, comment = "#>")
+```
+
+## Resources
+
+- reading:
+  - end of BDA3 ch. 4
+  - optional: BDA3 ch. 8, 14-18, 21
+- lectures:
+  - ['12.1 Frequency evaluation, hypothesis testing and variable selection'](https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=e998b5dd-bf8e-42da-9f7c-ab1700ca2702)
+  - ['12.2 Overview of modeling data collection, BDA3 Ch 8, linear models, BDA Ch 14-18, lasso, horseshoe and Gaussian processes, BDA3 Ch 21'](https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=c43c862a-a5a4-45da-9b27-ab1700e12012)
+- [slides](../slides/slides_extra.pdf)
+
+## Notes
+
+### Lecture 12.1 Frequency evaluation, hypothesis testing and variable selection
+
+- Bayesian vs. Frequentist
+  - Bayesian theory has epistemic and aleatory probabilities
+  - Frequency evaluations focus on frequency properties given aleatoric repetition of an observation and modeling
+- on "null hypothesis testing":
+  - often inappropriate to test the probability that a value is 0
+    - for continuous data, the probability of a single value is always 0
+    - "region of practical equivalence" (ROPE) is another option
+  - best to focus on describing the full posterior
+    - e.g. amount of the posterior greater than or less than an important value
+    - e.g. where most of the posterior density is (89% or 95% HDI)
+- be careful about only looking at marginal posteriors, too
+  - joint posterior distributions may be informative
+  - e.g. height and weight variables in beta-blocker model are highly correlated; both marginals overlap 0, but joint does not
+- most common statistical tests are linear models
+  - longer list with more illustrations: https://lindeloev.github.io/tests-as-linear
+
+| classical test      | Bayesian equivalent | in 'rstanarm'               |
+|---------------------|---------------------|-----------------------------|
+| t-test              | mean of data        | `stan_glm(y ~ 1)`           |
+| paired t-test       | mean of diffs       | `stan_glm((y1 - y2) ~ 1)`   |
+| Pearson correlation | linear model        | `stan_glm(y ~ 1 + x)`       |
+| two-sample t-test   | group means         | `stan_glm(y ~ 1 + gid)`     |
+| ANOVA               | hierarchical model  | `stan_glm(y ~ 1 + (1|gid))` |
+
+### Lecture 12.2 Overview of modeling data collection, BDA3 Ch 8, linear models, BDA Ch 14-18, lasso, horseshoe and Gaussian processes, BDA3 Ch 21
+
+- LASSO and Bayesian LASSO
+  - Bayesian LASSO uses Laplace distribution as a prior
+  - is equivalent to L1 penalty in MLE LASSO, but because we still integrate over the entire posterior, it does not have the same "sparsifying" effect
+  - therefore, Bayesian LASSO is empirically worse than MLE LASSO
+  - final thought: best to separate the process of prior selection, posterior inference, and decision analysis
+  - **regularized horseshoe prior** a better choice if you have prior information that only some of the covariates are informative
+
+![projpred selection vs LASSO](assets/12_extended-topics/slides-extra_s23.jpg)
diff --git a/docs/notes/12_extended-topics.html b/docs/notes/12_extended-topics.html
new file mode 100644
index 0000000..51b416f
--- /dev/null
+++ b/docs/notes/12_extended-topics.html
@@ -0,0 +1,2604 @@
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+  <meta name="generator" content="distill" />
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+    line-height: 1.7em;
+    margin-top: 60px;
+    margin-bottom: 0;
+    border-top: 1px solid rgba(0, 0, 0, 0.1);
+    color: rgba(0,0,0,0.5);
+    padding-top: 60px;
+    padding-bottom: 48px;
+  }
+
+  d-appendix h3 {
+    grid-column: page-start / text-start;
+    font-size: 15px;
+    font-weight: 500;
+    margin-top: 1em;
+    margin-bottom: 0;
+    color: rgba(0,0,0,0.65);
+  }
+
+  d-appendix h3 + * {
+    margin-top: 1em;
+  }
+
+  d-appendix ol {
+    padding: 0 0 0 15px;
+  }
+
+  @media (min-width: 768px) {
+    d-appendix ol {
+      padding: 0 0 0 30px;
+      margin-left: -30px;
+    }
+  }
+
+  d-appendix li {
+    margin-bottom: 1em;
+  }
+
+  d-appendix a {
+    color: rgba(0, 0, 0, 0.6);
+  }
+
+  d-appendix > * {
+    grid-column: text;
+  }
+
+  d-appendix > d-footnote-list,
+  d-appendix > d-citation-list,
+  d-appendix > distill-appendix {
+    grid-column: screen;
+  }
+
+  /* Include footnote styles here so they can be overridden */
+
+  d-footnote-list {
+    contain: layout style;
+  }
+
+  d-footnote-list > * {
+    grid-column: text;
+  }
+
+  d-footnote-list a.footnote-backlink {
+    color: rgba(0,0,0,0.3);
+    padding-left: 0.5em;
+  }
+
+
+
+  /* Anchor.js */
+
+  .anchorjs-link {
+    /*transition: all .25s linear; */
+    text-decoration: none;
+    border-bottom: none;
+  }
+  *:hover > .anchorjs-link {
+    margin-left: -1.125em !important;
+    text-decoration: none;
+    border-bottom: none;
+  }
+
+  /* Social footer */
+
+  .social_footer {
+    margin-top: 30px;
+    margin-bottom: 0;
+    color: rgba(0,0,0,0.67);
+  }
+
+  .disqus-comments {
+    margin-right: 30px;
+  }
+
+  .disqus-comment-count {
+    border-bottom: 1px solid rgba(0, 0, 0, 0.4);
+    cursor: pointer;
+  }
+
+  #disqus_thread {
+    margin-top: 30px;
+  }
+
+  .article-sharing a {
+    border-bottom: none;
+    margin-right: 8px;
+  }
+
+  .article-sharing a:hover {
+    border-bottom: none;
+  }
+
+  .sidebar-section.subscribe {
+    font-size: 12px;
+    line-height: 1.6em;
+  }
+
+  .subscribe p {
+    margin-bottom: 0.5em;
+  }
+
+
+  .article-footer .subscribe {
+    font-size: 15px;
+    margin-top: 45px;
+  }
+
+
+  .sidebar-section.custom {
+    font-size: 12px;
+    line-height: 1.6em;
+  }
+
+  .custom p {
+    margin-bottom: 0.5em;
+  }
+
+  /* Styles for listing layout (hide title) */
+  .layout-listing d-title, .layout-listing .d-title {
+    display: none;
+  }
+
+  /* Styles for posts lists (not auto-injected) */
+
+
+  .posts-with-sidebar {
+    padding-left: 45px;
+    padding-right: 45px;
+  }
+
+  .posts-list .description h2,
+  .posts-list .description p {
+    font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Oxygen, Ubuntu, Cantarell, "Fira Sans", "Droid Sans", "Helvetica Neue", Arial, sans-serif;
+  }
+
+  .posts-list .description h2 {
+    font-weight: 700;
+    border-bottom: none;
+    padding-bottom: 0;
+  }
+
+  .posts-list h2.post-tag {
+    border-bottom: 1px solid rgba(0, 0, 0, 0.2);
+    padding-bottom: 12px;
+  }
+  .posts-list {
+    margin-top: 60px;
+    margin-bottom: 24px;
+  }
+
+  .posts-list .post-preview {
+    text-decoration: none;
+    overflow: hidden;
+    display: block;
+    border-bottom: 1px solid rgba(0, 0, 0, 0.1);
+    padding: 24px 0;
+  }
+
+  .post-preview-last {
+    border-bottom: none !important;
+  }
+
+  .posts-list .posts-list-caption {
+    grid-column: screen;
+    font-weight: 400;
+  }
+
+  .posts-list .post-preview h2 {
+    margin: 0 0 6px 0;
+    line-height: 1.2em;
+    font-style: normal;
+    font-size: 24px;
+  }
+
+  .posts-list .post-preview p {
+    margin: 0 0 12px 0;
+    line-height: 1.4em;
+    font-size: 16px;
+  }
+
+  .posts-list .post-preview .thumbnail {
+    box-sizing: border-box;
+    margin-bottom: 24px;
+    position: relative;
+    max-width: 500px;
+  }
+  .posts-list .post-preview img {
+    width: 100%;
+    display: block;
+  }
+
+  .posts-list .metadata {
+    font-size: 12px;
+    line-height: 1.4em;
+    margin-bottom: 18px;
+  }
+
+  .posts-list .metadata > * {
+    display: inline-block;
+  }
+
+  .posts-list .metadata .publishedDate {
+    margin-right: 2em;
+  }
+
+  .posts-list .metadata .dt-authors {
+    display: block;
+    margin-top: 0.3em;
+    margin-right: 2em;
+  }
+
+  .posts-list .dt-tags {
+    display: block;
+    line-height: 1em;
+  }
+
+  .posts-list .dt-tags .dt-tag {
+    display: inline-block;
+    color: rgba(0,0,0,0.6);
+    padding: 0.3em 0.4em;
+    margin-right: 0.2em;
+    margin-bottom: 0.4em;
+    font-size: 60%;
+    border: 1px solid rgba(0,0,0,0.2);
+    border-radius: 3px;
+    text-transform: uppercase;
+    font-weight: 500;
+  }
+
+  .posts-list img {
+    opacity: 1;
+  }
+
+  .posts-list img[data-src] {
+    opacity: 0;
+  }
+
+  .posts-more {
+    clear: both;
+  }
+
+
+  .posts-sidebar {
+    font-size: 16px;
+  }
+
+  .posts-sidebar h3 {
+    font-size: 16px;
+    margin-top: 0;
+    margin-bottom: 0.5em;
+    font-weight: 400;
+    text-transform: uppercase;
+  }
+
+  .sidebar-section {
+    margin-bottom: 30px;
+  }
+
+  .categories ul {
+    list-style-type: none;
+    margin: 0;
+    padding: 0;
+  }
+
+  .categories li {
+    color: rgba(0, 0, 0, 0.8);
+    margin-bottom: 0;
+  }
+
+  .categories li>a {
+    border-bottom: none;
+  }
+
+  .categories li>a:hover {
+    border-bottom: 1px solid rgba(0, 0, 0, 0.4);
+  }
+
+  .categories .active {
+    font-weight: 600;
+  }
+
+  .categories .category-count {
+    color: rgba(0, 0, 0, 0.4);
+  }
+
+
+  @media(min-width: 768px) {
+    .posts-list .post-preview h2 {
+      font-size: 26px;
+    }
+    .posts-list .post-preview .thumbnail {
+      float: right;
+      width: 30%;
+      margin-bottom: 0;
+    }
+    .posts-list .post-preview .description {
+      float: left;
+      width: 45%;
+    }
+    .posts-list .post-preview .metadata {
+      float: left;
+      width: 20%;
+      margin-top: 8px;
+    }
+    .posts-list .post-preview p {
+      margin: 0 0 12px 0;
+      line-height: 1.5em;
+      font-size: 16px;
+    }
+    .posts-with-sidebar .posts-list {
+      float: left;
+      width: 75%;
+    }
+    .posts-with-sidebar .posts-sidebar {
+      float: right;
+      width: 20%;
+      margin-top: 60px;
+      padding-top: 24px;
+      padding-bottom: 24px;
+    }
+  }
+
+
+  /* Improve display for browsers without grid (IE/Edge <= 15) */
+
+  .downlevel {
+    line-height: 1.6em;
+    font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Oxygen, Ubuntu, Cantarell, "Fira Sans", "Droid Sans", "Helvetica Neue", Arial, sans-serif;
+    margin: 0;
+  }
+
+  .downlevel .d-title {
+    padding-top: 6rem;
+    padding-bottom: 1.5rem;
+  }
+
+  .downlevel .d-title h1 {
+    font-size: 50px;
+    font-weight: 700;
+    line-height: 1.1em;
+    margin: 0 0 0.5rem;
+  }
+
+  .downlevel .d-title p {
+    font-weight: 300;
+    font-size: 1.2rem;
+    line-height: 1.55em;
+    margin-top: 0;
+  }
+
+  .downlevel .d-byline {
+    padding-top: 0.8em;
+    padding-bottom: 0.8em;
+    font-size: 0.8rem;
+    line-height: 1.8em;
+  }
+
+  .downlevel .section-separator {
+    border: none;
+    border-top: 1px solid rgba(0, 0, 0, 0.1);
+  }
+
+  .downlevel .d-article {
+    font-size: 1.06rem;
+    line-height: 1.7em;
+    padding-top: 1rem;
+    padding-bottom: 2rem;
+  }
+
+
+  .downlevel .d-appendix {
+    padding-left: 0;
+    padding-right: 0;
+    max-width: none;
+    font-size: 0.8em;
+    line-height: 1.7em;
+    margin-bottom: 0;
+    color: rgba(0,0,0,0.5);
+    padding-top: 40px;
+    padding-bottom: 48px;
+  }
+
+  .downlevel .footnotes ol {
+    padding-left: 13px;
+  }
+
+  .downlevel .base-grid,
+  .downlevel .distill-header,
+  .downlevel .d-title,
+  .downlevel .d-abstract,
+  .downlevel .d-article,
+  .downlevel .d-appendix,
+  .downlevel .distill-appendix,
+  .downlevel .d-byline,
+  .downlevel .d-footnote-list,
+  .downlevel .d-citation-list,
+  .downlevel .distill-footer,
+  .downlevel .appendix-bottom,
+  .downlevel .posts-container {
+    padding-left: 40px;
+    padding-right: 40px;
+  }
+
+  @media(min-width: 768px) {
+    .downlevel .base-grid,
+    .downlevel .distill-header,
+    .downlevel .d-title,
+    .downlevel .d-abstract,
+    .downlevel .d-article,
+    .downlevel .d-appendix,
+    .downlevel .distill-appendix,
+    .downlevel .d-byline,
+    .downlevel .d-footnote-list,
+    .downlevel .d-citation-list,
+    .downlevel .distill-footer,
+    .downlevel .appendix-bottom,
+    .downlevel .posts-container {
+    padding-left: 150px;
+    padding-right: 150px;
+    max-width: 900px;
+  }
+  }
+
+  .downlevel pre code {
+    display: block;
+    border-left: 2px solid rgba(0, 0, 0, .1);
+    padding: 0 0 0 20px;
+    font-size: 14px;
+  }
+
+  .downlevel code, .downlevel pre {
+    color: black;
+    background: none;
+    font-family: Consolas, Monaco, 'Andale Mono', 'Ubuntu Mono', monospace;
+    text-align: left;
+    white-space: pre;
+    word-spacing: normal;
+    word-break: normal;
+    word-wrap: normal;
+    line-height: 1.5;
+
+    -moz-tab-size: 4;
+    -o-tab-size: 4;
+    tab-size: 4;
+
+    -webkit-hyphens: none;
+    -moz-hyphens: none;
+    -ms-hyphens: none;
+    hyphens: none;
+  }
+
+  .downlevel .posts-list .post-preview {
+    color: inherit;
+  }
+
+
+
+  </style>
+
+  <script type="application/javascript">
+
+  function is_downlevel_browser() {
+    if (bowser.isUnsupportedBrowser({ msie: "12", msedge: "16"},
+                                   window.navigator.userAgent)) {
+      return true;
+    } else {
+      return window.load_distill_framework === undefined;
+    }
+  }
+
+  // show body when load is complete
+  function on_load_complete() {
+
+    // add anchors
+    if (window.anchors) {
+      window.anchors.options.placement = 'left';
+      window.anchors.add('d-article > h2, d-article > h3, d-article > h4, d-article > h5');
+    }
+
+
+    // set body to visible
+    document.body.style.visibility = 'visible';
+
+    // force redraw for leaflet widgets
+    if (window.HTMLWidgets) {
+      var maps = window.HTMLWidgets.findAll(".leaflet");
+      $.each(maps, function(i, el) {
+        var map = this.getMap();
+        map.invalidateSize();
+        map.eachLayer(function(layer) {
+          if (layer instanceof L.TileLayer)
+            layer.redraw();
+        });
+      });
+    }
+
+    // trigger 'shown' so htmlwidgets resize
+    $('d-article').trigger('shown');
+  }
+
+  function init_distill() {
+
+    init_common();
+
+    // create front matter
+    var front_matter = $('<d-front-matter></d-front-matter>');
+    $('#distill-front-matter').wrap(front_matter);
+
+    // create d-title
+    $('.d-title').changeElementType('d-title');
+
+    // create d-byline
+    var byline = $('<d-byline></d-byline>');
+    $('.d-byline').replaceWith(byline);
+
+    // create d-article
+    var article = $('<d-article></d-article>');
+    $('.d-article').wrap(article).children().unwrap();
+
+    // move posts container into article
+    $('.posts-container').appendTo($('d-article'));
+
+    // create d-appendix
+    $('.d-appendix').changeElementType('d-appendix');
+
+    // flag indicating that we have appendix items
+    var appendix = $('.appendix-bottom').children('h3').length > 0;
+
+    // replace footnotes with <d-footnote>
+    $('.footnote-ref').each(function(i, val) {
+      appendix = true;
+      var href = $(this).attr('href');
+      var id = href.replace('#', '');
+      var fn = $('#' + id);
+      var fn_p = $('#' + id + '>p');
+      fn_p.find('.footnote-back').remove();
+      var text = fn_p.html();
+      var dtfn = $('<d-footnote></d-footnote>');
+      dtfn.html(text);
+      $(this).replaceWith(dtfn);
+    });
+    // remove footnotes
+    $('.footnotes').remove();
+
+    // move refs into #references-listing
+    $('#references-listing').replaceWith($('#refs'));
+
+    $('h1.appendix, h2.appendix').each(function(i, val) {
+      $(this).changeElementType('h3');
+    });
+    $('h3.appendix').each(function(i, val) {
+      var id = $(this).attr('id');
+      $('.d-contents a[href="#' + id + '"]').parent().remove();
+      appendix = true;
+      $(this).nextUntil($('h1, h2, h3')).addBack().appendTo($('d-appendix'));
+    });
+
+    // show d-appendix if we have appendix content
+    $("d-appendix").css('display', appendix ? 'grid' : 'none');
+
+    // localize layout chunks to just output
+    $('.layout-chunk').each(function(i, val) {
+
+      // capture layout
+      var layout = $(this).attr('data-layout');
+
+      // apply layout to markdown level block elements
+      var elements = $(this).children().not('details, div.sourceCode, pre, script');
+      elements.each(function(i, el) {
+        var layout_div = $('<div class="' + layout + '"></div>');
+        if (layout_div.hasClass('shaded')) {
+          var shaded_content = $('<div class="shaded-content"></div>');
+          $(this).wrap(shaded_content);
+          $(this).parent().wrap(layout_div);
+        } else {
+          $(this).wrap(layout_div);
+        }
+      });
+
+
+      // unwrap the layout-chunk div
+      $(this).children().unwrap();
+    });
+
+    // remove code block used to force  highlighting css
+    $('.distill-force-highlighting-css').parent().remove();
+
+    // remove empty line numbers inserted by pandoc when using a
+    // custom syntax highlighting theme
+    $('code.sourceCode a:empty').remove();
+
+    // load distill framework
+    load_distill_framework();
+
+    // wait for window.distillRunlevel == 4 to do post processing
+    function distill_post_process() {
+
+      if (!window.distillRunlevel || window.distillRunlevel < 4)
+        return;
+
+      // hide author/affiliations entirely if we have no authors
+      var front_matter = JSON.parse($("#distill-front-matter").html());
+      var have_authors = front_matter.authors && front_matter.authors.length > 0;
+      if (!have_authors)
+        $('d-byline').addClass('hidden');
+
+      // article with toc class
+      $('.d-contents').parent().addClass('d-article-with-toc');
+
+      // strip links that point to #
+      $('.authors-affiliations').find('a[href="#"]').removeAttr('href');
+
+      // add orcid ids
+      $('.authors-affiliations').find('.author').each(function(i, el) {
+        var orcid_id = front_matter.authors[i].orcidID;
+        if (orcid_id) {
+          var a = $('<a></a>');
+          a.attr('href', 'https://orcid.org/' + orcid_id);
+          var img = $('<img></img>');
+          img.addClass('orcid-id');
+          img.attr('alt', 'ORCID ID');
+          img.attr('src','data:image/png;base64,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');
+          a.append(img);
+          $(this).append(a);
+        }
+      });
+
+      // hide elements of author/affiliations grid that have no value
+      function hide_byline_column(caption) {
+        $('d-byline').find('h3:contains("' + caption + '")').parent().css('visibility', 'hidden');
+      }
+
+      // affiliations
+      var have_affiliations = false;
+      for (var i = 0; i<front_matter.authors.length; ++i) {
+        var author = front_matter.authors[i];
+        if (author.affiliation !== "&nbsp;") {
+          have_affiliations = true;
+          break;
+        }
+      }
+      if (!have_affiliations)
+        $('d-byline').find('h3:contains("Affiliations")').css('visibility', 'hidden');
+
+      // published date
+      if (!front_matter.publishedDate)
+        hide_byline_column("Published");
+
+      // document object identifier
+      var doi = $('d-byline').find('h3:contains("DOI")');
+      var doi_p = doi.next().empty();
+      if (!front_matter.doi) {
+        // if we have a citation and valid citationText then link to that
+        if ($('#citation').length > 0 && front_matter.citationText) {
+          doi.html('Citation');
+          $('<a href="#citation"></a>')
+            .text(front_matter.citationText)
+            .appendTo(doi_p);
+        } else {
+          hide_byline_column("DOI");
+        }
+      } else {
+        $('<a></a>')
+           .attr('href', "https://doi.org/" + front_matter.doi)
+           .html(front_matter.doi)
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+// Copyright (c) 2019 Bryan Braun; Licensed MIT
+//
+// @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt Expat
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\nd-byline {\n  contain: content;\n  overflow: hidden;\n  border-top: 1px solid rgba(0, 0, 0, 0.1);\n  font-size: 0.8rem;\n  line-height: 1.8em;\n  padding: 1.5rem 0;\n  min-height: 1.8em;\n}\n\n\nd-byline .byline {\n  grid-template-columns: 1fr 1fr;\n  grid-column: text;\n}\n\n@media(min-width: 768px) {\n  d-byline .byline {\n    grid-template-columns: 1fr 1fr 1fr 1fr;\n  }\n}\n\nd-byline .authors-affiliations {\n  grid-column-end: span 2;\n  grid-template-columns: 1fr 1fr;\n  margin-bottom: 1em;\n}\n\n@media(min-width: 768px) {\n  d-byline .authors-affiliations {\n    margin-bottom: 0;\n  }\n}\n\nd-byline h3 {\n  font-size: 0.6rem;\n  font-weight: 400;\n  color: rgba(0, 0, 0, 0.5);\n  margin: 0;\n  text-transform: uppercase;\n}\n\nd-byline p {\n  margin: 0;\n}\n\nd-byline a,\nd-article d-byline a {\n  color: rgba(0, 0, 0, 0.8);\n  text-decoration: none;\n  border-bottom: none;\n}\n\nd-article d-byline a:hover {\n  text-decoration: underline;\n  border-bottom: none;\n}\n\nd-byline p.author {\n  font-weight: 500;\n}\n\nd-byline .affiliations {\n\n}\n'+'/*\n * Copyright 2018 The Distill Template Authors\n *\n * Licensed under the Apache License, Version 2.0 (the "License");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an "AS IS" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\nd-article {\n  contain: layout style;\n  overflow-x: hidden;\n  border-top: 1px solid rgba(0, 0, 0, 0.1);\n  padding-top: 2rem;\n  color: rgba(0, 0, 0, 0.8);\n}\n\nd-article > * {\n  grid-column: text;\n}\n\n@media(min-width: 768px) {\n  d-article {\n    font-size: 16px;\n  }\n}\n\n@media(min-width: 1024px) {\n  d-article {\n    font-size: 1.06rem;\n    line-height: 1.7em;\n  }\n}\n\n\n/* H2 */\n\n\nd-article .marker {\n  text-decoration: none;\n  border: none;\n  counter-reset: section;\n  grid-column: kicker;\n  line-height: 1.7em;\n}\n\nd-article .marker:hover {\n  border: none;\n}\n\nd-article .marker span {\n  padding: 0 3px 4px;\n  border-bottom: 1px solid rgba(0, 0, 0, 0.2);\n  position: relative;\n  top: 4px;\n}\n\nd-article .marker:hover span {\n  color: rgba(0, 0, 0, 0.7);\n  border-bottom: 1px solid rgba(0, 0, 0, 0.7);\n}\n\nd-article h2 {\n  font-weight: 600;\n  font-size: 24px;\n  line-height: 1.25em;\n  margin: 2rem 0 1.5rem 0;\n  border-bottom: 1px solid rgba(0, 0, 0, 0.1);\n  padding-bottom: 1rem;\n}\n\n@media(min-width: 1024px) {\n  d-article h2 {\n    font-size: 36px;\n  }\n}\n\n/* H3 */\n\nd-article h3 {\n  font-weight: 700;\n  font-size: 18px;\n  line-height: 1.4em;\n  margin-bottom: 1em;\n  margin-top: 2em;\n}\n\n@media(min-width: 1024px) {\n  d-article h3 {\n    font-size: 20px;\n  }\n}\n\n/* H4 */\n\nd-article h4 {\n  font-weight: 600;\n  text-transform: uppercase;\n  font-size: 14px;\n  line-height: 1.4em;\n}\n\nd-article a {\n  color: inherit;\n}\n\nd-article p,\nd-article ul,\nd-article ol,\nd-article blockquote {\n  margin-top: 0;\n  margin-bottom: 1em;\n  margin-left: 0;\n  margin-right: 0;\n}\n\nd-article blockquote {\n  border-left: 2px solid rgba(0, 0, 0, 0.2);\n  padding-left: 2em;\n  font-style: italic;\n  color: rgba(0, 0, 0, 0.6);\n}\n\nd-article a {\n  border-bottom: 1px solid rgba(0, 0, 0, 0.4);\n  text-decoration: none;\n}\n\nd-article a:hover {\n  border-bottom: 1px solid rgba(0, 0, 0, 0.8);\n}\n\nd-article .link {\n  text-decoration: underline;\n  cursor: pointer;\n}\n\nd-article ul,\nd-article ol {\n  padding-left: 24px;\n}\n\nd-article li {\n  margin-bottom: 1em;\n  margin-left: 0;\n  padding-left: 0;\n}\n\nd-article li:last-child {\n  margin-bottom: 0;\n}\n\nd-article pre {\n  font-size: 14px;\n  margin-bottom: 20px;\n}\n\nd-article hr {\n  grid-column: screen;\n  width: 100%;\n  border: none;\n  border-bottom: 1px solid rgba(0, 0, 0, 0.1);\n  margin-top: 60px;\n  margin-bottom: 60px;\n}\n\nd-article section {\n  margin-top: 60px;\n  margin-bottom: 60px;\n}\n\nd-article span.equation-mimic {\n  font-family: georgia;\n  font-size: 115%;\n  font-style: italic;\n}\n\nd-article > d-code,\nd-article section > d-code  {\n  display: block;\n}\n\nd-article > d-math[block],\nd-article section > d-math[block]  {\n  display: block;\n}\n\n@media (max-width: 768px) {\n  d-article > d-code,\n  d-article section > d-code,\n  d-article > d-math[block],\n  d-article section > d-math[block] {\n      overflow-x: scroll;\n      -ms-overflow-style: none;  // IE 10+\n      overflow: -moz-scrollbars-none;  // Firefox\n  }\n\n  d-article > d-code::-webkit-scrollbar,\n  d-article section > d-code::-webkit-scrollbar,\n  d-article > d-math[block]::-webkit-scrollbar,\n  d-article section > d-math[block]::-webkit-scrollbar {\n    display: none;  // Safari and Chrome\n  }\n}\n\nd-article .citation {\n  color: #668;\n  cursor: pointer;\n}\n\nd-include {\n  width: auto;\n  display: block;\n}\n\nd-figure {\n  contain: layout style;\n}\n\n/* KaTeX */\n\n.katex, .katex-prerendered {\n  contain: style;\n  display: inline-block;\n}\n\n/* Tables */\n\nd-article table {\n  border-collapse: collapse;\n  margin-bottom: 1.5rem;\n  border-bottom: 1px solid rgba(0, 0, 0, 0.2);\n}\n\nd-article table th {\n  border-bottom: 1px solid rgba(0, 0, 0, 0.2);\n}\n\nd-article table td {\n  border-bottom: 1px solid rgba(0, 0, 0, 0.05);\n}\n\nd-article table tr:last-of-type td {\n  border-bottom: none;\n}\n\nd-article table th,\nd-article table td {\n  font-size: 15px;\n  padding: 2px 8px;\n}\n\nd-article table tbody :first-child td {\n  padding-top: 2px;\n}\n'+ni+'/*\n * Copyright 2018 The Distill Template Authors\n *\n * Licensed under the Apache License, Version 2.0 (the "License");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *      http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an "AS IS" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@media print {\n\n  @page {\n    size: 8in 11in;\n    @bottom-right {\n      content: counter(page) " of " counter(pages);\n    }\n  }\n\n  html {\n    /* no general margins -- CSS Grid takes care of those */\n  }\n\n  p, code {\n    page-break-inside: avoid;\n  }\n\n  h2, h3 {\n    page-break-after: avoid;\n  }\n\n  d-header {\n    visibility: hidden;\n  }\n\n  d-footer {\n    display: none!important;\n  }\n\n}\n',mi=[{name:'WebComponents',support:function(){return'customElements'in window&&'attachShadow'in Element.prototype&&'getRootNode'in Element.prototype&&'content'in document.createElement('template')&&'Promise'in window&&'from'in Array},url:'https://distill.pub/third-party/polyfills/webcomponents-lite.js'},{name:'IntersectionObserver',support:function(){return'IntersectionObserver'in window&&'IntersectionObserverEntry'in window},url:'https://distill.pub/third-party/polyfills/intersection-observer.js'}];class yi{static browserSupportsAllFeatures(){return mi.every((e)=>e.support())}static load(e){const t=function(t){t.loaded=!0,console.info('Runlevel 0: Polyfill has finished loading: '+t.name),yi.neededPolyfills.every((e)=>e.loaded)&&(console.info('Runlevel 0: All required polyfills have finished loading.'),console.info('Runlevel 0->1.'),window.distillRunlevel=1,e())};for(const n of yi.neededPolyfills)g(n,t)}static get neededPolyfills(){return yi._neededPolyfills||(yi._neededPolyfills=mi.filter((e)=>!e.support())),yi._neededPolyfills}}const xi=ti('d-abstract',`
+<style>
+  :host {
+    font-size: 1.25rem;
+    line-height: 1.6em;
+    color: rgba(0, 0, 0, 0.7);
+    -webkit-font-smoothing: antialiased;
+  }
+
+  ::slotted(p) {
+    margin-top: 0;
+    margin-bottom: 1em;
+    grid-column: text-start / middle-end;
+  }
+  ${function(e){return`${e} {
+      grid-column: left / text;
+    }
+  `}('d-abstract')}
+</style>
+
+<slot></slot>
+`);class ki extends xi(HTMLElement){}const vi=ti('d-appendix',`
+<style>
+
+d-appendix {
+  contain: layout style;
+  font-size: 0.8em;
+  line-height: 1.7em;
+  margin-top: 60px;
+  margin-bottom: 0;
+  border-top: 1px solid rgba(0, 0, 0, 0.1);
+  color: rgba(0,0,0,0.5);
+  padding-top: 60px;
+  padding-bottom: 48px;
+}
+
+d-appendix h3 {
+  grid-column: page-start / text-start;
+  font-size: 15px;
+  font-weight: 500;
+  margin-top: 1em;
+  margin-bottom: 0;
+  color: rgba(0,0,0,0.65);
+}
+
+d-appendix h3 + * {
+  margin-top: 1em;
+}
+
+d-appendix ol {
+  padding: 0 0 0 15px;
+}
+
+@media (min-width: 768px) {
+  d-appendix ol {
+    padding: 0 0 0 30px;
+    margin-left: -30px;
+  }
+}
+
+d-appendix li {
+  margin-bottom: 1em;
+}
+
+d-appendix a {
+  color: rgba(0, 0, 0, 0.6);
+}
+
+d-appendix > * {
+  grid-column: text;
+}
+
+d-appendix > d-footnote-list,
+d-appendix > d-citation-list,
+d-appendix > distill-appendix {
+  grid-column: screen;
+}
+
+</style>
+
+`,!1);class wi extends vi(HTMLElement){}const Si=/^\s*$/;class Ci extends HTMLElement{static get is(){return'd-article'}constructor(){super(),new MutationObserver((e)=>{for(const t of e)for(const e of t.addedNodes)switch(e.nodeName){case'#text':{const t=e.nodeValue;if(!Si.test(t)){console.warn('Use of unwrapped text in distill articles is discouraged as it breaks layout! Please wrap any text in a <span> or <p> tag. We found the following text: '+t);const n=document.createElement('span');n.innerHTML=e.nodeValue,e.parentNode.insertBefore(n,e),e.parentNode.removeChild(e)}}}}).observe(this,{childList:!0})}}var Ti='undefined'==typeof window?'undefined'==typeof global?'undefined'==typeof self?{}:self:global:window,_i=f(function(e,t){(function(e){function t(){this.months=['jan','feb','mar','apr','may','jun','jul','aug','sep','oct','nov','dec'],this.notKey=[',','{','}',' ','='],this.pos=0,this.input='',this.entries=[],this.currentEntry='',this.setInput=function(e){this.input=e},this.getEntries=function(){return this.entries},this.isWhitespace=function(e){return' '==e||'\r'==e||'\t'==e||'\n'==e},this.match=function(e,t){if((void 0==t||null==t)&&(t=!0),this.skipWhitespace(t),this.input.substring(this.pos,this.pos+e.length)==e)this.pos+=e.length;else throw'Token mismatch, expected '+e+', found '+this.input.substring(this.pos);this.skipWhitespace(t)},this.tryMatch=function(e,t){return(void 0==t||null==t)&&(t=!0),this.skipWhitespace(t),this.input.substring(this.pos,this.pos+e.length)==e},this.matchAt=function(){for(;this.input.length>this.pos&&'@'!=this.input[this.pos];)this.pos++;return!('@'!=this.input[this.pos])},this.skipWhitespace=function(e){for(;this.isWhitespace(this.input[this.pos]);)this.pos++;if('%'==this.input[this.pos]&&!0==e){for(;'\n'!=this.input[this.pos];)this.pos++;this.skipWhitespace(e)}},this.value_braces=function(){var e=0;this.match('{',!1);for(var t=this.pos,n=!1;;){if(!n)if('}'==this.input[this.pos]){if(0<e)e--;else{var i=this.pos;return this.match('}',!1),this.input.substring(t,i)}}else if('{'==this.input[this.pos])e++;else if(this.pos>=this.input.length-1)throw'Unterminated value';n='\\'==this.input[this.pos]&&!1==n,this.pos++}},this.value_comment=function(){for(var e='',t=0;!(this.tryMatch('}',!1)&&0==t);){if(e+=this.input[this.pos],'{'==this.input[this.pos]&&t++,'}'==this.input[this.pos]&&t--,this.pos>=this.input.length-1)throw'Unterminated 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this.input.substring(e,this.pos);this.pos++}},this.key_equals_value=function(){var e=this.key();if(this.tryMatch('=')){this.match('=');var t=this.value();return[e,t]}throw'... = value expected, equals sign missing:'+this.input.substring(this.pos)},this.key_value_list=function(){var e=this.key_equals_value();for(this.currentEntry.entryTags={},this.currentEntry.entryTags[e[0]]=e[1];this.tryMatch(',')&&(this.match(','),!this.tryMatch('}'));)e=this.key_equals_value(),this.currentEntry.entryTags[e[0]]=e[1]},this.entry_body=function(e){this.currentEntry={},this.currentEntry.citationKey=this.key(),this.currentEntry.entryType=e.substring(1),this.match(','),this.key_value_list(),this.entries.push(this.currentEntry)},this.directive=function(){return this.match('@'),'@'+this.key()},this.preamble=function(){this.currentEntry={},this.currentEntry.entryType='PREAMBLE',this.currentEntry.entry=this.value_comment(),this.entries.push(this.currentEntry)},this.comment=function(){this.currentEntry={},this.currentEntry.entryType='COMMENT',this.currentEntry.entry=this.value_comment(),this.entries.push(this.currentEntry)},this.entry=function(e){this.entry_body(e)},this.bibtex=function(){for(;this.matchAt();){var e=this.directive();this.match('{'),'@STRING'==e?this.string():'@PREAMBLE'==e?this.preamble():'@COMMENT'==e?this.comment():this.entry(e),this.match('}')}}}e.toJSON=function(e){var n=new t;return n.setInput(e),n.bibtex(),n.entries},e.toBibtex=function(e){var t='';for(var n in e){if(t+='@'+e[n].entryType,t+='{',e[n].citationKey&&(t+=e[n].citationKey+', '),e[n].entry&&(t+=e[n].entry),e[n].entryTags){var i='';for(var a in e[n].entryTags)0!=i.length&&(i+=', '),i+=a+'= {'+e[n].entryTags[a]+'}';t+=i}t+='}\n\n'}return t}})(t)});class Li extends HTMLElement{static get is(){return'd-bibliography'}constructor(){super();const e=new MutationObserver((e)=>{for(const t of e)('SCRIPT'===t.target.nodeName||'characterData'===t.type)&&this.parseIfPossible()});e.observe(this,{childList:!0,characterData:!0,subtree:!0})}connectedCallback(){requestAnimationFrame(()=>{this.parseIfPossible()})}parseIfPossible(){const e=this.querySelector('script');if(e)if('text/bibtex'==e.type){const t=e.textContent;if(this.bibtex!==t){this.bibtex=t;const e=b(this.bibtex);this.notify(e)}}else if('text/json'==e.type){const t=new Map(JSON.parse(e.textContent));this.notify(t)}else console.warn('Unsupported bibliography script tag type: '+e.type)}notify(e){const t=new CustomEvent('onBibliographyChanged',{detail:e,bubbles:!0});this.dispatchEvent(t)}static get observedAttributes(){return['src']}receivedBibtex(e){const t=b(e.target.response);this.notify(t)}attributeChangedCallback(e,t,n){var i=new XMLHttpRequest;i.onload=(t)=>this.receivedBibtex(t),i.onerror=()=>console.warn(`Could not load Bibtex! (tried ${n})`),i.responseType='text',i.open('GET',n,!0),i.send()}}class Ai extends HTMLElement{static get is(){return'd-byline'}set frontMatter(e){this.innerHTML=y(e)}}const Ei=ti('d-cite',`
+<style>
+
+:host {
+
+}
+
+.citation {
+  display: inline-block;
+  color: hsla(206, 90%, 20%, 0.7);
+}
+
+.citation-number {
+  cursor: default;
+  white-space: nowrap;
+  font-family: -apple-system, BlinkMacSystemFont, "Roboto", Helvetica, sans-serif;
+  font-size: 75%;
+  color: hsla(206, 90%, 20%, 0.7);
+  display: inline-block;
+  line-height: 1.1em;
+  text-align: center;
+  position: relative;
+  top: -2px;
+  margin: 0 2px;
+}
+
+figcaption .citation-number {
+  font-size: 11px;
+  font-weight: normal;
+  top: -2px;
+  line-height: 1em;
+}
+
+ul {
+  margin: 0;
+  padding: 0;
+  list-style-type: none;
+}
+
+ul li {
+  padding: 15px 10px 15px 10px;
+  border-bottom: 1px solid rgba(0,0,0,0.1)
+}
+
+ul li:last-of-type {
+  border-bottom: none;
+}
+
+</style>
+
+<d-hover-box id="hover-box"></d-hover-box>
+
+<div id="citation-" class="citation">
+  <slot></slot>
+  <span class="citation-number"></span>
+</div>
+`);class Di extends Ei(HTMLElement){connectedCallback(){this.outerSpan=this.root.querySelector('#citation-'),this.innerSpan=this.root.querySelector('.citation-number'),this.hoverBox=this.root.querySelector('d-hover-box'),window.customElements.whenDefined('d-hover-box').then(()=>{this.hoverBox.listen(this)})}static get observedAttributes(){return['key']}attributeChangedCallback(e,t,n){const i=t?'onCiteKeyChanged':'onCiteKeyCreated',a=n.split(','),d={detail:[this,a],bubbles:!0},r=new CustomEvent(i,d);document.dispatchEvent(r)}set key(e){this.setAttribute('key',e)}get key(){return this.getAttribute('key')}get keys(){return this.getAttribute('key').split(',')}set numbers(e){const t=e.map((e)=>{return-1==e?'?':e+1+''}),n='['+t.join(', ')+']';this.innerSpan&&(this.innerSpan.textContent=n)}set entries(e){this.hoverBox&&(this.hoverBox.innerHTML=`<ul>
+      ${e.map(l).map((e)=>`<li>${e}</li>`).join('\n')}
+      </ul>`)}}const Mi=`
+d-citation-list {
+  contain: layout style;
+}
+
+d-citation-list .references {
+  grid-column: text;
+}
+
+d-citation-list .references .title {
+  font-weight: 500;
+}
+`;class Oi extends HTMLElement{static get is(){return'd-citation-list'}connectedCallback(){this.hasAttribute('distill-prerendered')||(this.style.display='none')}set citations(e){x(this,e)}}var Ui=f(function(e){var t='undefined'==typeof window?'undefined'!=typeof WorkerGlobalScope&&self instanceof WorkerGlobalScope?self:{}:window,n=function(){var e=/\blang(?:uage)?-(\w+)\b/i,n=0,a=t.Prism={util:{encode:function(e){return e instanceof i?new i(e.type,a.util.encode(e.content),e.alias):'Array'===a.util.type(e)?e.map(a.util.encode):e.replace(/&/g,'&amp;').replace(/</g,'&lt;').replace(/\u00a0/g,' ')},type:function(e){return Object.prototype.toString.call(e).match(/\[object (\w+)\]/)[1]},objId:function(e){return e.__id||Object.defineProperty(e,'__id',{value:++n}),e.__id},clone:function(e){var t=a.util.type(e);switch(t){case'Object':var n={};for(var i in e)e.hasOwnProperty(i)&&(n[i]=a.util.clone(e[i]));return n;case'Array':return e.map&&e.map(function(e){return a.util.clone(e)});}return 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Prism.languages.go['class-name'],Prism.languages.markdown=Prism.languages.extend('markup',{}),Prism.languages.insertBefore('markdown','prolog',{blockquote:{pattern:/^>(?:[\t ]*>)*/m,alias:'punctuation'},code:[{pattern:/^(?: {4}|\t).+/m,alias:'keyword'},{pattern:/``.+?``|`[^`\n]+`/,alias:'keyword'}],title:[{pattern:/\w+.*(?:\r?\n|\r)(?:==+|--+)/,alias:'important',inside:{punctuation:/==+$|--+$/}},{pattern:/(^\s*)#+.+/m,lookbehind:!0,alias:'important',inside:{punctuation:/^#+|#+$/}}],hr:{pattern:/(^\s*)([*-])([\t ]*\2){2,}(?=\s*$)/m,lookbehind:!0,alias:'punctuation'},list:{pattern:/(^\s*)(?:[*+-]|\d+\.)(?=[\t ].)/m,lookbehind:!0,alias:'punctuation'},"url-reference":{pattern:/!?\[[^\]]+\]:[\t ]+(?:\S+|<(?:\\.|[^>\\])+>)(?:[\t 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?\[[^\]\n]*\])/,inside:{variable:{pattern:/(!?\[)[^\]]+(?=\]$)/,lookbehind:!0},string:{pattern:/"(?:\\.|[^"\\])*"(?=\)$)/}}}}),Prism.languages.markdown.bold.inside.url=Prism.util.clone(Prism.languages.markdown.url),Prism.languages.markdown.italic.inside.url=Prism.util.clone(Prism.languages.markdown.url),Prism.languages.markdown.bold.inside.italic=Prism.util.clone(Prism.languages.markdown.italic),Prism.languages.markdown.italic.inside.bold=Prism.util.clone(Prism.languages.markdown.bold),Prism.languages.julia={comment:{pattern:/(^|[^\\])#.*/,lookbehind:!0},string:/"""[\s\S]+?"""|'''[\s\S]+?'''|("|')(\\?.)*?\1/,keyword:/\b(abstract|baremodule|begin|bitstype|break|catch|ccall|const|continue|do|else|elseif|end|export|finally|for|function|global|if|immutable|import|importall|let|local|macro|module|print|println|quote|return|try|type|typealias|using|while)\b/,boolean:/\b(true|false)\b/,number:/\b-?(0[box])?(?:[\da-f]+\.?\d*|\.\d+)(?:[efp][+-]?\d+)?j?\b/i,operator:/\+=?|-=?|\*=?|\/[\/=]?|\\=?|\^=?|%=?|÷=?|!=?=?|&=?|\|[=>]?|\$=?|<(?:<=?|[=:])?|>(?:=|>>?=?)?|==?=?|[~≠≤≥]/,punctuation:/[{}[\];(),.:]/};const Ii=ti('d-code',`
+<style>
+
+code {
+  white-space: nowrap;
+  background: rgba(0, 0, 0, 0.04);
+  border-radius: 2px;
+  padding: 4px 7px;
+  font-size: 15px;
+  color: rgba(0, 0, 0, 0.6);
+}
+
+pre code {
+  display: block;
+  border-left: 2px solid rgba(0, 0, 0, .1);
+  padding: 0 0 0 36px;
+}
+
+${'/**\n * prism.js default theme for JavaScript, CSS and HTML\n * Based on dabblet (http://dabblet.com)\n * @author Lea Verou\n */\n\ncode[class*="language-"],\npre[class*="language-"] {\n\tcolor: black;\n\tbackground: none;\n\ttext-shadow: 0 1px white;\n\tfont-family: Consolas, Monaco, \'Andale Mono\', \'Ubuntu Mono\', monospace;\n\ttext-align: left;\n\twhite-space: pre;\n\tword-spacing: normal;\n\tword-break: normal;\n\tword-wrap: normal;\n\tline-height: 1.5;\n\n\t-moz-tab-size: 4;\n\t-o-tab-size: 4;\n\ttab-size: 4;\n\n\t-webkit-hyphens: none;\n\t-moz-hyphens: none;\n\t-ms-hyphens: none;\n\thyphens: none;\n}\n\npre[class*="language-"]::-moz-selection, pre[class*="language-"] ::-moz-selection,\ncode[class*="language-"]::-moz-selection, code[class*="language-"] ::-moz-selection {\n\ttext-shadow: none;\n\tbackground: #b3d4fc;\n}\n\npre[class*="language-"]::selection, pre[class*="language-"] ::selection,\ncode[class*="language-"]::selection, code[class*="language-"] ::selection {\n\ttext-shadow: none;\n\tbackground: #b3d4fc;\n}\n\n@media print {\n\tcode[class*="language-"],\n\tpre[class*="language-"] {\n\t\ttext-shadow: none;\n\t}\n}\n\n/* Code blocks */\npre[class*="language-"] {\n\tpadding: 1em;\n\tmargin: .5em 0;\n\toverflow: auto;\n}\n\n:not(pre) > code[class*="language-"],\npre[class*="language-"] {\n\tbackground: #f5f2f0;\n}\n\n/* Inline code */\n:not(pre) > code[class*="language-"] {\n\tpadding: .1em;\n\tborder-radius: .3em;\n\twhite-space: normal;\n}\n\n.token.comment,\n.token.prolog,\n.token.doctype,\n.token.cdata {\n\tcolor: slategray;\n}\n\n.token.punctuation {\n\tcolor: #999;\n}\n\n.namespace {\n\topacity: .7;\n}\n\n.token.property,\n.token.tag,\n.token.boolean,\n.token.number,\n.token.constant,\n.token.symbol,\n.token.deleted {\n\tcolor: #905;\n}\n\n.token.selector,\n.token.attr-name,\n.token.string,\n.token.char,\n.token.builtin,\n.token.inserted {\n\tcolor: #690;\n}\n\n.token.operator,\n.token.entity,\n.token.url,\n.language-css .token.string,\n.style .token.string {\n\tcolor: #a67f59;\n\tbackground: hsla(0, 0%, 100%, .5);\n}\n\n.token.atrule,\n.token.attr-value,\n.token.keyword {\n\tcolor: #07a;\n}\n\n.token.function {\n\tcolor: #DD4A68;\n}\n\n.token.regex,\n.token.important,\n.token.variable {\n\tcolor: #e90;\n}\n\n.token.important,\n.token.bold {\n\tfont-weight: bold;\n}\n.token.italic {\n\tfont-style: italic;\n}\n\n.token.entity {\n\tcursor: help;\n}\n'}
+</style>
+
+<code id="code-container"></code>
+
+`);class Ni extends ei(Ii(HTMLElement)){renderContent(){if(this.languageName=this.getAttribute('language'),!this.languageName)return void console.warn('You need to provide a language attribute to your <d-code> block to let us know how to highlight your code; e.g.:\n <d-code language="python">zeros = np.zeros(shape)</d-code>.');const e=Ui.languages[this.languageName];if(void 0==e)return void console.warn(`Distill does not yet support highlighting your code block in "${this.languageName}'.`);let t=this.textContent;const n=this.shadowRoot.querySelector('#code-container');if(this.hasAttribute('block')){t=t.replace(/\n/,'');const e=t.match(/\s*/);if(t=t.replace(new RegExp('\n'+e,'g'),'\n'),t=t.trim(),n.parentNode instanceof ShadowRoot){const e=document.createElement('pre');this.shadowRoot.removeChild(n),e.appendChild(n),this.shadowRoot.appendChild(e)}}n.className=`language-${this.languageName}`,n.innerHTML=Ui.highlight(t,e)}}const ji=ti('d-footnote',`
+<style>
+
+d-math[block] {
+  display: block;
+}
+
+:host {
+
+}
+
+sup {
+  line-height: 1em;
+  font-size: 0.75em;
+  position: relative;
+  top: -.5em;
+  vertical-align: baseline;
+}
+
+span {
+  color: hsla(206, 90%, 20%, 0.7);
+  cursor: default;
+}
+
+.footnote-container {
+  padding: 10px;
+}
+
+</style>
+
+<d-hover-box>
+  <div class="footnote-container">
+    <slot id="slot"></slot>
+  </div>
+</d-hover-box>
+
+<sup>
+  <span id="fn-" data-hover-ref=""></span>
+</sup>
+
+`);class Ri extends ji(HTMLElement){constructor(){super();const e=new MutationObserver(this.notify);e.observe(this,{childList:!0,characterData:!0,subtree:!0})}notify(){const e={detail:this,bubbles:!0},t=new CustomEvent('onFootnoteChanged',e);document.dispatchEvent(t)}connectedCallback(){this.hoverBox=this.root.querySelector('d-hover-box'),window.customElements.whenDefined('d-hover-box').then(()=>{this.hoverBox.listen(this)}),Ri.currentFootnoteId+=1;const e=Ri.currentFootnoteId.toString();this.root.host.id='d-footnote-'+e;const t='dt-fn-hover-box-'+e;this.hoverBox.id=t;const n=this.root.querySelector('#fn-');n.setAttribute('id','fn-'+e),n.setAttribute('data-hover-ref',t),n.textContent=e}}Ri.currentFootnoteId=0;const qi=ti('d-footnote-list',`
+<style>
+
+d-footnote-list {
+  contain: layout style;
+}
+
+d-footnote-list > * {
+  grid-column: text;
+}
+
+d-footnote-list a.footnote-backlink {
+  color: rgba(0,0,0,0.3);
+  padding-left: 0.5em;
+}
+
+</style>
+
+<h3>Footnotes</h3>
+<ol></ol>
+`,!1);class Fi extends qi(HTMLElement){connectedCallback(){super.connectedCallback(),this.list=this.root.querySelector('ol'),this.root.style.display='none'}set footnotes(e){if(this.list.innerHTML='',e.length){this.root.style.display='';for(const t of e){const e=document.createElement('li');e.id=t.id+'-listing',e.innerHTML=t.innerHTML;const n=document.createElement('a');n.setAttribute('class','footnote-backlink'),n.textContent='[\u21A9]',n.href='#'+t.id,e.appendChild(n),this.list.appendChild(e)}}else this.root.style.display='none'}}const Pi=ti('d-hover-box',`
+<style>
+
+:host {
+  position: absolute;
+  width: 100%;
+  left: 0px;
+  z-index: 10000;
+  display: none;
+  white-space: normal
+}
+
+.container {
+  position: relative;
+  width: 704px;
+  max-width: 100vw;
+  margin: 0 auto;
+}
+
+.panel {
+  position: absolute;
+  font-size: 1rem;
+  line-height: 1.5em;
+  top: 0;
+  left: 0;
+  width: 100%;
+  border: 1px solid rgba(0, 0, 0, 0.1);
+  background-color: rgba(250, 250, 250, 0.95);
+  box-shadow: 0 0 7px rgba(0, 0, 0, 0.1);
+  border-radius: 4px;
+  box-sizing: border-box;
+
+  backdrop-filter: blur(2px);
+  -webkit-backdrop-filter: blur(2px);
+}
+
+</style>
+
+<div class="container">
+  <div class="panel">
+    <slot></slot>
+  </div>
+</div>
+`);class Hi extends Pi(HTMLElement){constructor(){super()}connectedCallback(){}listen(e){this.bindDivEvents(this),this.bindTriggerEvents(e)}bindDivEvents(e){e.addEventListener('mouseover',()=>{this.visible||this.showAtNode(e),this.stopTimeout()}),e.addEventListener('mouseout',()=>{this.extendTimeout(500)}),e.addEventListener('touchstart',(e)=>{e.stopPropagation()},{passive:!0}),document.body.addEventListener('touchstart',()=>{this.hide()},{passive:!0})}bindTriggerEvents(e){e.addEventListener('mouseover',()=>{this.visible||this.showAtNode(e),this.stopTimeout()}),e.addEventListener('mouseout',()=>{this.extendTimeout(300)}),e.addEventListener('touchstart',(t)=>{this.visible?this.hide():this.showAtNode(e),t.stopPropagation()},{passive:!0})}show(e){this.visible=!0,this.style.display='block',this.style.top=Pn(e[1]+10)+'px'}showAtNode(e){const t=e.getBoundingClientRect();this.show([e.offsetLeft+t.width,e.offsetTop+t.height])}hide(){this.visible=!1,this.style.display='none',this.stopTimeout()}stopTimeout(){this.timeout&&clearTimeout(this.timeout)}extendTimeout(e){this.stopTimeout(),this.timeout=setTimeout(()=>{this.hide()},e)}}class zi extends HTMLElement{static get is(){return'd-title'}}const Yi=ti('d-references',`
+<style>
+d-references {
+  display: block;
+}
+</style>
+`,!1);class Bi extends Yi(HTMLElement){}class Wi extends HTMLElement{static get is(){return'd-toc'}connectedCallback(){this.getAttribute('prerendered')||(window.onload=()=>{const e=document.querySelector('d-article'),t=e.querySelectorAll('h2, h3');k(this,t)})}}class Vi extends HTMLElement{static get is(){return'd-figure'}static get readyQueue(){return Vi._readyQueue||(Vi._readyQueue=[]),Vi._readyQueue}static addToReadyQueue(e){-1===Vi.readyQueue.indexOf(e)&&(Vi.readyQueue.push(e),Vi.runReadyQueue())}static runReadyQueue(){const e=Vi.readyQueue.sort((e,t)=>e._seenOnScreen-t._seenOnScreen).filter((e)=>!e._ready).pop();e&&(e.ready(),requestAnimationFrame(Vi.runReadyQueue))}constructor(){super(),this._ready=!1,this._onscreen=!1,this._offscreen=!0}connectedCallback(){this.loadsWhileScrolling=this.hasAttribute('loadsWhileScrolling'),Vi.marginObserver.observe(this),Vi.directObserver.observe(this)}disconnectedCallback(){Vi.marginObserver.unobserve(this),Vi.directObserver.unobserve(this)}static get marginObserver(){if(!Vi._marginObserver){const e=window.innerHeight,t=Fn(2*e),n=Vi.didObserveMarginIntersection,i=new IntersectionObserver(n,{rootMargin:t+'px 0px '+t+'px 0px',threshold:0.01});Vi._marginObserver=i}return Vi._marginObserver}static didObserveMarginIntersection(e){for(const t of e){const e=t.target;t.isIntersecting&&!e._ready&&Vi.addToReadyQueue(e)}}static get directObserver(){return Vi._directObserver||(Vi._directObserver=new IntersectionObserver(Vi.didObserveDirectIntersection,{rootMargin:'0px',threshold:[0,1]})),Vi._directObserver}static didObserveDirectIntersection(e){for(const t of e){const e=t.target;t.isIntersecting?(e._seenOnScreen=new Date,e._offscreen&&e.onscreen()):e._onscreen&&e.offscreen()}}addEventListener(e,t){super.addEventListener(e,t),'ready'===e&&-1!==Vi.readyQueue.indexOf(this)&&(this._ready=!1,Vi.runReadyQueue()),'onscreen'===e&&this.onscreen()}ready(){this._ready=!0,Vi.marginObserver.unobserve(this);const e=new CustomEvent('ready');this.dispatchEvent(e)}onscreen(){this._onscreen=!0,this._offscreen=!1;const e=new CustomEvent('onscreen');this.dispatchEvent(e)}offscreen(){this._onscreen=!1,this._offscreen=!0;const e=new CustomEvent('offscreen');this.dispatchEvent(e)}}if('undefined'!=typeof window){Vi.isScrolling=!1;let e;window.addEventListener('scroll',()=>{Vi.isScrolling=!0,clearTimeout(e),e=setTimeout(()=>{Vi.isScrolling=!1,Vi.runReadyQueue()},500)},!0)}const Ki=ti('d-interstitial',`
+<style>
+
+.overlay {
+  position: fixed;
+  width: 100%;
+  height: 100%;
+  top: 0;
+  left: 0;
+  background: white;
+
+  opacity: 1;
+  visibility: visible;
+
+  display: flex;
+  flex-flow: column;
+  justify-content: center;
+  z-index: 2147483647 /* MaxInt32 */
+
+}
+
+.container {
+  position: relative;
+  margin-left: auto;
+  margin-right: auto;
+  max-width: 420px;
+  padding: 2em;
+}
+
+h1 {
+  text-decoration: underline;
+  text-decoration-color: hsl(0,100%,40%);
+  -webkit-text-decoration-color: hsl(0,100%,40%);
+  margin-bottom: 1em;
+  line-height: 1.5em;
+}
+
+input[type="password"] {
+  -webkit-appearance: none;
+  -moz-appearance: none;
+  appearance: none;
+  -webkit-box-shadow: none;
+  -moz-box-shadow: none;
+  box-shadow: none;
+  -webkit-border-radius: none;
+  -moz-border-radius: none;
+  -ms-border-radius: none;
+  -o-border-radius: none;
+  border-radius: none;
+  outline: none;
+
+  font-size: 18px;
+  background: none;
+  width: 25%;
+  padding: 10px;
+  border: none;
+  border-bottom: solid 2px #999;
+  transition: border .3s;
+}
+
+input[type="password"]:focus {
+  border-bottom: solid 2px #333;
+}
+
+input[type="password"].wrong {
+  border-bottom: solid 2px hsl(0,100%,40%);
+}
+
+p small {
+  color: #888;
+}
+
+.logo {
+  position: relative;
+  font-size: 1.5em;
+  margin-bottom: 3em;
+}
+
+.logo svg {
+  width: 36px;
+  position: relative;
+  top: 6px;
+  margin-right: 2px;
+}
+
+.logo svg path {
+  fill: none;
+  stroke: black;
+  stroke-width: 2px;
+}
+
+</style>
+
+<div class="overlay">
+  <div class="container">
+    <h1>This article is in review.</h1>
+    <p>Do not share this URL or the contents of this article. Thank you!</p>
+    <input id="interstitial-password-input" type="password" name="password" autofocus/>
+    <p><small>Enter the password we shared with you as part of the review process to view the article.</small></p>
+  </div>
+</div>
+`);class $i extends Ki(HTMLElement){connectedCallback(){if(this.shouldRemoveSelf())this.parentElement.removeChild(this);else{const e=this.root.querySelector('#interstitial-password-input');e.oninput=(e)=>this.passwordChanged(e)}}passwordChanged(e){const t=e.target.value;t===this.password&&(console.log('Correct password entered.'),this.parentElement.removeChild(this),'undefined'!=typeof Storage&&(console.log('Saved that correct password was entered.'),localStorage.setItem(this.localStorageIdentifier(),'true')))}shouldRemoveSelf(){return window&&window.location.hostname==='distill.pub'?(console.warn('Interstitial found on production, hiding it.'),!0):'undefined'!=typeof Storage&&'true'===localStorage.getItem(this.localStorageIdentifier())&&(console.log('Loaded that correct password was entered before; skipping interstitial.'),!0)}localStorageIdentifier(){return'distill-drafts'+(window?window.location.pathname:'-')+'interstitial-password-correct'}}var Xi=function(e,t){return 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+<div class="content">
+  <a href="/" class="logo">
+    ${Or}
+    Distill
+  </a>
+  <nav class="nav">
+    <a href="/about/">About</a>
+    <a href="/prize/">Prize</a>
+    <a href="/journal/">Submit</a>
+  </nav>
+</div>
+`,!1);class Ir extends Ur(HTMLElement){}const Nr=`
+<style>
+  distill-appendix {
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+  text-decoration: none;
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+
+</style>
+
+<div class='container'>
+
+  <a href="/" class="logo">
+    ${Or}
+    Distill
+  </a> is dedicated to clear explanations of machine learning
+
+  <div class="nav">
+    <a href="https://distill.pub/about/">About</a>
+    <a href="https://distill.pub/journal/">Submit</a>
+    <a href="https://distill.pub/prize/">Prize</a>
+    <a href="https://distill.pub/archive/">Archive</a>
+    <a href="https://distill.pub/rss.xml">RSS</a>
+    <a href="https://github.com/distillpub">GitHub</a>
+    <a href="https://twitter.com/distillpub">Twitter</a>
+    &nbsp;&nbsp;&nbsp;&nbsp; ISSN 2476-0757
+  </div>
+
+</div>
+
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+{"title":"12. Extended topics","authors":[],"publishedDate":"2021-12-02T00:00:00.000+00:00"}
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+
+<div class="d-title">
+<h1>12. Extended topics</h1>
+<!--radix_placeholder_categories-->
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+
+</div>
+
+
+<div class="d-article">
+<h2 id="resources">Resources</h2>
+<ul>
+<li>reading:
+<ul>
+<li>end of BDA3 ch. 4</li>
+<li>optional: BDA3 ch. 8, 14-18, 21</li>
+</ul></li>
+<li>lectures:
+<ul>
+<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=e998b5dd-bf8e-42da-9f7c-ab1700ca2702">‘12.1 Frequency evaluation, hypothesis testing and variable selection’</a></li>
+<li><a href="https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=c43c862a-a5a4-45da-9b27-ab1700e12012">‘12.2 Overview of modeling data collection, BDA3 Ch 8, linear models, BDA Ch 14-18, lasso, horseshoe and Gaussian processes, BDA3 Ch 21’</a></li>
+</ul></li>
+<li><a href="../slides/slides_extra.pdf">slides</a></li>
+</ul>
+<h2 id="notes">Notes</h2>
+<h3 id="lecture-12.1-frequency-evaluation-hypothesis-testing-and-variable-selection">Lecture 12.1 Frequency evaluation, hypothesis testing and variable selection</h3>
+<ul>
+<li>Bayesian vs. Frequentist
+<ul>
+<li>Bayesian theory has epistemic and aleatory probabilities</li>
+<li>Frequency evaluations focus on frequency properties given aleatoric repetition of an observation and modeling</li>
+</ul></li>
+<li>on “null hypothesis testing”:
+<ul>
+<li>often inappropriate to test the probability that a value is 0
+<ul>
+<li>for continuous data, the probability of a single value is always 0</li>
+<li>“region of practical equivalence” (ROPE) is another option</li>
+</ul></li>
+<li>best to focus on describing the full posterior
+<ul>
+<li>e.g. amount of the posterior greater than or less than an important value</li>
+<li>e.g. where most of the posterior density is (89% or 95% HDI)</li>
+</ul></li>
+</ul></li>
+<li>be careful about only looking at marginal posteriors, too
+<ul>
+<li>joint posterior distributions may be informative</li>
+<li>e.g. height and weight variables in beta-blocker model are highly correlated; both marginals overlap 0, but joint does not</li>
+</ul></li>
+<li>most common statistical tests are linear models
+<ul>
+<li>longer list with more illustrations: <a href="https://lindeloev.github.io/tests-as-linear" class="uri">https://lindeloev.github.io/tests-as-linear</a></li>
+</ul></li>
+</ul>
+<table>
+<thead>
+<tr class="header">
+<th>classical test</th>
+<th>Bayesian equivalent</th>
+<th>in ‘rstanarm’</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td>t-test</td>
+<td>mean of data</td>
+<td><code>stan_glm(y ~ 1)</code></td>
+</tr>
+<tr class="even">
+<td>paired t-test</td>
+<td>mean of diffs</td>
+<td><code>stan_glm((y1 - y2) ~ 1)</code></td>
+</tr>
+<tr class="odd">
+<td>Pearson correlation</td>
+<td>linear model</td>
+<td><code>stan_glm(y ~ 1 + x)</code></td>
+</tr>
+<tr class="even">
+<td>two-sample t-test</td>
+<td>group means</td>
+<td><code>stan_glm(y ~ 1 + gid)</code></td>
+</tr>
+<tr class="odd">
+<td>ANOVA</td>
+<td>hierarchical model</td>
+<td><code>stan_glm(y ~ 1 + (1|gid))</code></td>
+</tr>
+</tbody>
+</table>
+<h3 id="lecture-12.2-overview-of-modeling-data-collection-bda3-ch-8-linear-models-bda-ch-14-18-lasso-horseshoe-and-gaussian-processes-bda3-ch-21">Lecture 12.2 Overview of modeling data collection, BDA3 Ch 8, linear models, BDA Ch 14-18, lasso, horseshoe and Gaussian processes, BDA3 Ch 21</h3>
+<ul>
+<li>LASSO and Bayesian LASSO
+<ul>
+<li>Bayesian LASSO uses Laplace distribution as a prior</li>
+<li>is equivalent to L1 penalty in MLE LASSO, but because we still integrate over the entire posterior, it does not have the same “sparsifying” effect</li>
+<li>therefore, Bayesian LASSO is empirically worse than MLE LASSO</li>
+<li>final thought: best to separate the process of prior selection, posterior inference, and decision analysis</li>
+<li><strong>regularized horseshoe prior</strong> a better choice if you have prior information that only some of the covariates are informative</li>
+</ul></li>
+</ul>
+<figure>
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alt="projpred selection vs LASSO" /><figcaption aria-hidden="true">projpred selection vs LASSO</figcaption>
+</figure>
+<div class="sourceCode" id="cb1"><pre class="sourceCode r distill-force-highlighting-css"><code class="sourceCode r"></code></pre></div>
+<!--radix_placeholder_article_footer-->
+<!--/radix_placeholder_article_footer-->
+</div>
+
+<div class="d-appendix">
+</div>
+
+
+<!--radix_placeholder_site_after_body-->
+<!--/radix_placeholder_site_after_body-->
+<!--radix_placeholder_appendices-->
+<div class="appendix-bottom"></div>
+<!--/radix_placeholder_appendices-->
+<!--radix_placeholder_navigation_after_body-->
+<!--/radix_placeholder_navigation_after_body-->
+
+</body>
+
+</html>
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diff --git a/docs/project-guidelines.html b/docs/project-guidelines.html
index 5eaac40..d41823c 100644
--- a/docs/project-guidelines.html
+++ b/docs/project-guidelines.html
@@ -2318,6 +2318,7 @@ <h3>${suggestion.title}</h3>
 <a href="notes/09_model-selection_bda3-7.html">Section 9. Model comparison and selection</a>
 <a href="notes/10_decision-analysis_bda3-9.html">Section 10. Decision analysis</a>
 <a href="notes/11_normal-approx-freq-properties_bda3-04.html">Section 11. Normal approximation &amp; Frequency properties</a>
+<a href="notes/12_extended-topics.html">Section 12. Extended topics</a>
 </div>
 </div>
 <div class="nav-dropdown">
diff --git a/docs/search.json b/docs/search.json
index f12a985..08d6e89 100644
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@@ -6,7 +6,7 @@
       "description": "Some additional details about the website",
       "author": [],
       "contents": "\n\n\n\n",
-      "last_modified": "2021-12-02T06:27:56-05:00"
+      "last_modified": "2021-12-03T07:05:36-05:00"
     },
     {
       "path": "index.html",
@@ -18,14 +18,14 @@
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-      "contents": "\nResources\nCourse website\n2021 Schedule\nGitHub repo (my fork)\nBayesian Data Analysis (3e) (BDA3) (exercise solutions)\nChapter Notes\nVideo lectures or individually lists here\nLecture slides\nHow to study\n\nThe following are recommendations from the course creators on how to take the course.\n\nThe recommended way to go through the material is:\nRead the reading instructions for a chapter in the chapter notes.\nRead the chapter in BDA3 and check that you find the terms listed in the reading instructions.\nWatch the corresponding video lecture to get explanations for most important parts.\nRead corresponding additional information in the chapter notes.\nRun the corresponding demos in R demos or Python demos.\nRead the exercise instructions and make the corresponding assignments. Demo codes in R demos and Python demos have a lot of useful examples for handling data and plotting figures. If you have problems, visit TA sessions or ask in course slack channel.\nIf you want to learn more, make also self study exercises listed below.\nSections\nSection\nNotes\nBook exercises\nAssignments\n1. Course Introduction\nnotes\nexercises\nassignment 1\n2. Basics of Bayesian Inference\nnotes\nexercises\nassignment 2\n3. Multidimensional Posterior\nnotes\nexercises\nassignment 3\n4. Monte Carlo\nnotes\nexercises\nassignment 4\n5. Markov chain Monte Carlo\nnotes\nexercises\nassignment 5\n6. HMC, NUTS, and Stan\nnotes\nexercises\nassignment 6\n7. Hierarchical models and exchangeability\nnotes\nexercises\nassignment 7\n8. Model checking & Cross-validation\nnotes\n(none)\n(none)\n9. Model comparison and selection\nnotes\n(none)\nassignment 8\n10. Decision analysis\nnotes\n(none)\nassignment 9\n11. Normal approximation & Frequency properties\nnotes\n(none)\n(none)\nStan models\nDrug bioassay model for Assignment 6\n8 school SAT model\nDrownings for Assignment 7\nFactory machine measurements for Assignments 7 & 8:\npooled\nseparate\nhierarchical (also used in Assignment 9)\n\n",
-      "last_modified": "2021-12-02T06:27:58-05:00"
+      "contents": "\nResources\nCourse website\n2021 Schedule\nGitHub repo (my fork)\nBayesian Data Analysis (3e) (BDA3) (exercise solutions)\nChapter Notes\nVideo lectures or individually lists here\nLecture slides\nHow to study\n\nThe following are recommendations from the course creators on how to take the course.\n\nThe recommended way to go through the material is:\nRead the reading instructions for a chapter in the chapter notes.\nRead the chapter in BDA3 and check that you find the terms listed in the reading instructions.\nWatch the corresponding video lecture to get explanations for most important parts.\nRead corresponding additional information in the chapter notes.\nRun the corresponding demos in R demos or Python demos.\nRead the exercise instructions and make the corresponding assignments. Demo codes in R demos and Python demos have a lot of useful examples for handling data and plotting figures. If you have problems, visit TA sessions or ask in course slack channel.\nIf you want to learn more, make also self study exercises listed below.\nSections\nSection\nNotes\nBook exercises\nAssignments\n1. Course Introduction\nnotes\nexercises\nassignment 1\n2. Basics of Bayesian Inference\nnotes\nexercises\nassignment 2\n3. Multidimensional Posterior\nnotes\nexercises\nassignment 3\n4. Monte Carlo\nnotes\nexercises\nassignment 4\n5. Markov chain Monte Carlo\nnotes\nexercises\nassignment 5\n6. HMC, NUTS, and Stan\nnotes\nexercises\nassignment 6\n7. Hierarchical models and exchangeability\nnotes\nexercises\nassignment 7\n8. Model checking & Cross-validation\nnotes\n(none)\n(none)\n9. Model comparison and selection\nnotes\n(none)\nassignment 8\n10. Decision analysis\nnotes\n(none)\nassignment 9\n11. Normal approximation & Frequency properties\nnotes\n(none)\n(none)\n12. Extended topics\nnotes\n(none)\n(none)\nStan models\nDrug bioassay model for Assignment 6\n8 school SAT model\nDrownings for Assignment 7\nFactory machine measurements for Assignments 7 & 8:\npooled\nseparate\nhierarchical (also used in Assignment 9)\n\n",
+      "last_modified": "2021-12-03T07:05:37-05:00"
     },
     {
       "path": "project-guidelines.html",
       "author": [],
       "contents": "\nProject work details\nProject work involves choosing a data set and performing a whole analysis according to all the parts of Bayesian workflow studied along the course.\nThe project work is meant to be done in period II.\nIn the beginning of the period II\nForm a group. We prefer groups of 3, but the project can be done in groups of 1-2.\nSelect a topic. You may ask in the course chat channel #project for opinion whether it’s a good topic and a good dataset. You can change the topic later.\nStart planning.\n\n\nThe main work for the project and the presentation will be done in the second half of the period II after all the workflow parts have been discussed in the course.\nThe online presentations will be made on the evaluation week after period II.\nAll suspected plagiarism will be reported and investigated. See more about the Aalto University Code of Academic Integrity and Handling Violations Thereof.\nProject schedule\nForm a group and pick a topic. Register the group before end of 8th Nov, 2021.\nGroups of 3 can reserve a presentation slot starting TBA.\nGroups of 2 can reserve a presentation slot starting TBA.\nGroups of 1 can reserve a presentation slot starting TBA.\nGroups that register late can reserve a presentation slot starting TBA.\nWork on the project. TA session queue is also for project questions.\nProject report deadline December 6, 2021. Submit in peergrade (separate “class”, the link will be added to MyCourses).\nProject report peer grading December 7-9, 2021 (so that you’ll get feedback for the report before the presentations).\nProject presentations December 13-17, 2021.\nGroups\nProject work is done in groups of 1-3 persons. Preferred group size is 3, because you learn more when you talk about the project with someone else.\nIf you don’t have a group, you can ask other students in the group chat channel #project. Tell what kind of data you are interested in (e.g. medicine, health, biological, engineering, political, business), whether you prefer R or Python, and whether you have already more concrete idea for the topic.\nGroups of 3 students can choose their presentation time slot before 1-2 student groups. 3 person group is expected to do a bit more work than 1-2 person groups.\nYou can do the project alone, but the amount of work is expected to the same for 2 person groups.\nTA sessions\nThe groups will get help for the project work in TA sessions. When there are no weekly assignments, the TA sessions are still organized for helping in the project work.\nEvaluation\nThe project work’s evaluation consists of:\npeer-graded project report (40%) (within peergrade submission 80% and feedack 20%)\npresentation and oral exam graded by the course staff (60%)\nclarity of slides + use of figures\nclarity of oral presentation + flow of the presentation\nall required parts included (not necessarily all in main slides, but it needs to be clear that all required steps were performed)\naccuracy of use of terms (oral exam)\nresponses to questions (oral exam)\n\nProject report\nIn the project report you practice presenting the problem and data analysis results, which means that minimal listing of code and figures is not a good report. There are different levels for how data analysis project could be reported. This report should be more than a summary of results without workflow steps. While describing the steps and decisions made during the workflow, to keep the report readable some of the diagnostic outputs and code can be put in the appendix. If you are uncertain you can ask TAs in TA sessions whether you are on a good level of amount of details.\nThe report should include\nIntroduction describing\nthe motivation\nthe problem\nand the main modeling idea.\nShowing some illustrative figure is recommended.\nDescription of the data and the analysis problem. Provide information where the data was obtained, and if it has been previously used in some online case study and how your analysis differs from the existing analyses.\nDescription of at least two models, for example:\nnon hierarchical and hierarchical,\nlinear and non linear,\nvariable selection with many models.\nInformative or weakly informative priors, and justification of their choices.\nStan, rstanarm or brms code.\nHow to the Stan model was run, that is, what options were used. This is also more clear as combination of textual explanation and the actual code line.\nConvergence diagnostics (\\(\\widehat{R}\\), ESS, divergences) and what was done if the convergence was not good with the first try.\nPosterior predictive checks and what was done to improve the model.\nModel comparison (e.g. with LOO-CV).\nPredictive performance assessment if applicable (e.g. classification accuracy) and evaluation of practical usefulness of the accuracy.\nSensitivity analysis with respect to prior choices (i.e. checking whether the result changes a lot if prior is changed)\nDiscussion of issues and potential improvements.\nConclusion what was learned from the data analysis.\nSelf-reflection of what the group learned while making the project.\nProject presentation\nIn addition to the submitted report, each project must be presented by the authoring group, according to the following guidelines:\nThe presentation should be high level but sufficiently detailed information should be readily available to help answering questions from the audience.\nThe duration of the presentation should be 10 minutes (groups of 1-2 students) or 15 minutes (groups of 3 students).\nAt the end of the presentation there will be an extra 5-10 minutes of questions by anyone in the audience or two members of the course staff who are present. The questions from lecturer/TAs can be considered as an oral exam questions, and if answers to these questions reveal weak knowledge of the methods and workflow steps which should be part of the project, that can reduce the grade.\nGrading will be done by the two members of the course staff using standardized grading instructions.\nSpecific recommendations for the presentations include:\nThe first slide should include project’s title and group members’ names.\nThe chosen statistical model(s), including observation model and priors, must be explained and justified,\nMake sure the font is big enough to be easily readable by the audience. This includes figure captions, legends and axis information,\nThe last slide should be a summary or take-home-messages and include contact information or link to a further information. (The grade will be reduced by one if the last slide has only something like “Thank you” or “Questions?”),\nIn general, the best presentations are often given by teams that have frequently attended TA sessions and gotten feedback, so we strongly recommend attending these sessions.\nMore details on the presentation sessions\nIf you don’t have microphone or video camera (e.g. in your laptop or mobile phone) then we’ll arrange your presentation on campus in period III.\nIf you reserved a presentation slot but need to cancel, do it asap.\nZoom meeting link for all time slots available in the course chat.\nAs we have many presentation in each slot join the meeting in time. Late arrivals will lower the grade. Very late arrivals will fail the presentation and can present in period III.\nPresenting group needs to have video and audio on.\nIt is easiest if just one from the group shares the slides, but it is expected that all group members present some part of the presentation orally.\nPresentation time is 10 min for 1-2 person groups and 15min for 3 person groups\nTime limit is strict. It’s good idea to practice the talk so that you get the timing right. Staff will announce 2min and 1min left and time ended. Going overtime reduces the grade.\nAfter the presentation there will be 5min for questions, answers, and feedback.\nEach student has to come up with at least one question during the session. Students can ask more questions. Questions by students are posted in chat, and they can be posted already during the presentation.\nStaff Will ask further questions (kind of oral exam)\nGrading of the project presentation takes int account\nclarity of slides + use of figures\nclarity of oral presentation + flow of the presentation\nall required parts included (not necessarily all in main slides, but it needs to be clear that all required steps were performed)\naccuracy of use of terms (oral exam)\nresponses to questions (oral exam)\n\nStudents will also self-evaluate their project. After the presentation each student who just presented sends a private message to one of the staff members with a self evaluation grade from themselves and for each group member (if applicable).\nData sets\nAs some data sets have been overused for these particular goals, note that the following ones are forbidden in this work (more can be added to this list so make sure to check it regularly):\nextremly common data sets like titanic, mtcars, iris\nBaseball batting (used by Bob Carpenter’s StanCon case study).\nData sets used in the course demos\nIt’s best to use a dataset for which there is no ready made analysis in internet, but if you choose a dataset used already in some online case study, provide the link to previous studies and report how your analysis differs from those (for example if someone has made non-Bayesian analysis and you do the full Bayesian analysis).\nDepending on the model and the structure of the data, a good data set would have more than 100 observations but less than 1 million. If you know an interesting big data set, you can use a smaller subset of the data to keep the computation times feasible. It would be good that the data has some structure, so that it is sensible to use multilevel/hierarchical models.\nModel requirements\nEvery parameter needs to have an explicit proper prior. Improper flat priors are not allowed.\nA hierarchical model is a model where the prior of certain parameter contain other parameters that are also estimated in the model. For instance, b ~ normal(mu, sigma), mu ~ normal(0, 1), sigma ~ exponential(1).\nDo not impose hard constrains on a parameter unless they are natural to them. uniform(a, b) should not be used unless the boundaries are really logical boundaries and values beyond the boundaries are completely impossible.\nAt least some models should include covariates. Modelling the outcome without predictors is likely too simple for the project.\nbrms can be used, but the Stan code must be included, briefly commented, and all priors need to be checked from the Stan code and adjusted to be weakly informative based on some justified explanation.\nSome examples\nThe following case study examples demonstrate how text, equations, figures, and code, and inference results can be included in one report. These examples don’t necessarily have all the workflow steps required in your report, but different steps are illustrated in different case studies and you can get good ideas for your report just by browsing through them.\nBDA R and Python demos are quite minimal in description of the data and discussion of the results, but show many diagnostics and basic plots.\nSome Stan case studies focus on some specific methods, but there are many case studies that are excellent examples for this course. They don’t include all the steps required in this course, but are good examples of writing. Some of them are longer or use more advanced models than required in this course.\nBayesian workflow for disease transmission modeling in Stan\nModel-based Inference for Causal Effects in Completely Randomized Experments\nTagging Basketball Events with HMM in Stan\nModel building and expansion for golf putting\nA Dyadic Item Response Theory Model\nPredator-Prey Population Dynamics: the Lotka-Volterra model in Stan\nSome StanCon case studies (scroll down) can also provide good project ideas.\n",
-      "last_modified": "2021-12-02T06:27:59-05:00"
+      "last_modified": "2021-12-03T07:05:37-05:00"
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   ],
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diff --git a/docs/sitemap.xml b/docs/sitemap.xml
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-    <lastmod>2021-12-02T06:26:56-05:00</lastmod>
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   <url>
     <loc>https://jhrcook.github.io/bayesian-data-analysis-course/project-guidelines.html</loc>
-    <lastmod>2021-12-02T06:27:58-05:00</lastmod>
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