improve title logos

Presentations/machine_learning_week_2023/revenue_retention_presentation.html

@@ -394,7 +394,7 @@
   <div class="reveal">
     <div class="slides">
 
-<section id="title-slide" data-background-image="revenue_retention_presentation_files/images/revenue_retention_presentation_files/juanitorduz_logo_small.png" data-background-opacity="0.2" class="quarto-title-block center">
+<section id="title-slide" class="quarto-title-block center">
   <h1 class="title">Cohort Revenue &amp; Retention Analysis: A Bayesian Approach</h1>
   <p class="subtitle">Machine Learning Week - 2023</p>
 
@@ -882,7 +882,7 @@ <h2>Open Source Packages</h2>
 <h2>Thank you!</h2>
 <p><a href="https://juanitorduz.github.io/">juanitorduz.github.io</a></p>
 <p><img data-src="revenue_retention_presentation_files/images/revenue_retention_presentation_files/juanitorduz.png" class="absolute" style="top: 0px; right: 0px; width: 600px; height: 600px; "></p>
-<p><img src="revenue_retention_presentation_files/images/revenue_retention_presentation_files/juanitorduz_logo_small.png" class="slide-logo"></p>
+<p><img src="revenue_retention_presentation_files/images/revenue_retention_presentation_files/wolt_logo.png" class="slide-logo"></p>
 <div class="footer footer-default">
 <p><a href="https://juanitorduz.github.io/revenue_retention/">Cohort Revenue &amp; Retention Analysis: A Bayesian Approach</a></p>
 </div>

Presentations/machine_learning_week_2023/revenue_retention_presentation.qmd

@@ -1,8 +1,5 @@
 ---
 title: "Cohort Revenue & Retention Analysis: A Bayesian Approach"
-title-slide-attributes:
-  data-background-image: revenue_retention_presentation_files/images/revenue_retention_presentation_files/juanitorduz_logo_small.png
-  data-background-opacity: "0.2"
 subtitle: Machine Learning Week - 2023
 author: 
   - name: Dr. Juan Orduz
@@ -11,7 +8,7 @@ author:
       - name: Mathematician & Data Scientist
 format:
   revealjs:
-    logo: revenue_retention_presentation_files/images/revenue_retention_presentation_files/juanitorduz_logo_small.png
+    logo: revenue_retention_presentation_files/images/revenue_retention_presentation_files/wolt_logo.png
     transition: none
     slide-number: true
     chalkboard: 

Presentations/pydata_2023/revenue_retention_presentation.html

@@ -8,7 +8,7 @@
 <link href="revenue_retention_presentation_files/libs/quarto-html/light-border.css" rel="stylesheet">
 <link href="revenue_retention_presentation_files/libs/quarto-html/quarto-html.min.css" rel="stylesheet" data-mode="light">
 <link href="revenue_retention_presentation_files/libs/quarto-html/quarto-syntax-highlighting.css" rel="stylesheet" id="quarto-text-highlighting-styles"><meta charset="utf-8">
-  <meta name="generator" content="quarto-1.3.433">
+  <meta name="generator" content="quarto-1.3.450">
 
   <meta name="author" content="Dr.&nbsp;Juan Orduz">
   <title>Cohort Revenue &amp; Retention Analysis: A Bayesian Approach</title>
@@ -394,7 +394,7 @@
   <div class="reveal">
     <div class="slides">
 
-<section id="title-slide" class="quarto-title-block center">
+<section id="title-slide" data-background-image="revenue_retention_presentation_files/images/revenue_retention_presentation_files/juanitorduz_logo_small.png" data-background-opacity="0.2" class="quarto-title-block center">
   <h1 class="title">Cohort Revenue &amp; Retention Analysis: A Bayesian Approach</h1>
   <p class="subtitle">PyData Berlin - July 2023</p>
 
@@ -641,13 +641,13 @@ <h2>Bayesian Additive Regression Trees</h2>
 <div>
 <ul>
 <li class="fragment"><p>Bayesian <strong>“sum-of-trees”</strong> model where each tree is constrained by a regularization prior to be a weak learner.</p></li>
-<li class="fragment"><p>To fit the sum-of-trees model, BART uses a tailored version of Bayesian backfitting MCMC that <strong>iteratively constructs and fits successive residuals</strong>.</p></li>
+<li class="fragment"><p>To fit the sum-of-trees model, BART uses <strong>PGBART</strong>, an inference algorithm based on the particle Gibbs method.</p></li>
 <li class="fragment"><p>BART depends on the <strong>number of trees</strong> <span class="math inline">\(m\in \mathbb{N}\)</span> and <strong>prior parameters</strong> <span class="math inline">\(\alpha \in (0, 1)\)</span> and <span class="math inline">\(\beta \in [0, \infty)\)</span> so that the probability that a node at depth <span class="math inline">\(d \in \mathbb{N}_{0}\)</span> is nonterminal is <span class="math inline">\(\alpha(1 + d)^{-\beta}\)</span>.</p></li>
 <li class="fragment"><p>BART is implemented in <a href="https://github.com/pymc-devs/pymc-bart"><code>pymc-bart</code></a>.</p></li>
 </ul>
 </div>
 <div class="footer">
-<p><a href="https://projecteuclid.org/journals/annals-of-applied-statistics/volume-4/issue-1/BART-Bayesian-additive-regression-trees/10.1214/09-AOAS285.full">BART: Bayesian additive regression trees</a></p>
+<p>See <a href="https://projecteuclid.org/journals/annals-of-applied-statistics/volume-4/issue-1/BART-Bayesian-additive-regression-trees/10.1214/09-AOAS285.full">BART: Bayesian additive regression trees</a> and <a href="https://arxiv.org/abs/2206.03619">Bayesian additive regression trees for probabilistic programming</a></p>
 </div>
 </section>
 <section id="bart-retention-model" class="slide level2">
@@ -882,7 +882,7 @@ <h2>Open Source Packages</h2>
 <h2>Thank you!</h2>
 <p><a href="https://juanitorduz.github.io/">juanitorduz.github.io</a></p>
 <p><img data-src="revenue_retention_presentation_files/images/revenue_retention_presentation_files/juanitorduz.png" class="absolute" style="top: 0px; right: 0px; width: 600px; height: 600px; "></p>
-<p><img src="revenue_retention_presentation_files/images/revenue_retention_presentation_files/juanitorduz_logo.gif" class="slide-logo"></p>
+<p><img src="revenue_retention_presentation_files/images/revenue_retention_presentation_files/juanitorduz_logo_small.png" class="slide-logo"></p>
 <div class="footer footer-default">
 <p><a href="https://juanitorduz.github.io/revenue_retention/">Cohort Revenue &amp; Retention Analysis: A Bayesian Approach</a></p>
 </div>

Presentations/pydata_2023/revenue_retention_presentation.qmd

@@ -1,5 +1,8 @@
 ---
 title: "Cohort Revenue & Retention Analysis: A Bayesian Approach"
+title-slide-attributes:
+  data-background-image: revenue_retention_presentation_files/images/revenue_retention_presentation_files/juanitorduz_logo_small.png
+  data-background-opacity: "0.2"
 subtitle: PyData Berlin - July 2023
 author: 
   - name: Dr. Juan Orduz
@@ -8,7 +11,7 @@ author:
       - name: Mathematician & Data Scientist
 format:
   revealjs:
-    logo: revenue_retention_presentation_files/images/revenue_retention_presentation_files/juanitorduz_logo.gif
+    logo: revenue_retention_presentation_files/images/revenue_retention_presentation_files/juanitorduz_logo_small.png
     transition: none
     slide-number: true
     chalkboard: 
@@ -309,7 +312,7 @@ We need a more flexible model that can capture **non-linearities and interaction
 
 - Bayesian **"sum-of-trees”** model where each tree is constrained by a regularization prior to be a weak learner.
 
-- To fit the sum-of-trees model, BART uses a tailored version of Bayesian backfitting MCMC that **iteratively constructs and fits successive residuals**.
+- To fit the sum-of-trees model, BART uses **PGBART**, an inference algorithm based on the particle Gibbs method.
 
 - BART depends on the **number of trees** $m\in \mathbb{N}$ and **prior parameters** $\alpha \in (0, 1)$ and $\beta \in [0, \infty)$ so that the probability that a node at depth $d \in \mathbb{N}_{0}$ is nonterminal is 
 $\alpha(1 + d)^{-\beta}$.
@@ -318,7 +321,7 @@ $\alpha(1 + d)^{-\beta}$.
 :::
 
 ::: footer
-[BART: Bayesian additive regression trees](https://projecteuclid.org/journals/annals-of-applied-statistics/volume-4/issue-1/BART-Bayesian-additive-regression-trees/10.1214/09-AOAS285.full)
+See [BART: Bayesian additive regression trees](https://projecteuclid.org/journals/annals-of-applied-statistics/volume-4/issue-1/BART-Bayesian-additive-regression-trees/10.1214/09-AOAS285.full) and [Bayesian additive regression trees for probabilistic programming](https://arxiv.org/abs/2206.03619)
 :::
 
 ## BART Retention Model