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juanitorduz · Nov 14, 2023 · 5d6ae13 · 5d6ae13
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diff --git a/Presentations/intro_hierarchical_models/intro_hierarchical_models.html b/Presentations/intro_hierarchical_models/intro_hierarchical_models.html
@@ -8,7 +8,7 @@
 <link href="intro_hierarchical_models_files/libs/quarto-html/light-border.css" rel="stylesheet">
 <link href="intro_hierarchical_models_files/libs/quarto-html/quarto-html.min.css" rel="stylesheet" data-mode="light">
 <link href="intro_hierarchical_models_files/libs/quarto-html/quarto-syntax-highlighting.css" rel="stylesheet" id="quarto-text-highlighting-styles"><meta charset="utf-8">
-  <meta name="generator" content="quarto-1.3.450">
+  <meta name="generator" content="quarto-1.3.353">
 
   <meta name="author" content="Dr.&nbsp;Juan Orduz">
   <title>Introduction to Hierarchical Models</title>
@@ -422,7 +422,7 @@ <h2>Outline</h2>
 <li class="fragment">Pooled</li>
 <li class="fragment">Hierarchical
 <ul>
-<li class="fragment">Non-Ceneter Parametrization</li>
+<li class="fragment">Non-Centered Parametrization</li>
 <li class="fragment">Shrinkage Effect</li>
 </ul></li>
 <li class="fragment">Hierarchical with Correlated Random Effects</li>
@@ -464,7 +464,7 @@ <h3 id="books">Books</h3>
 </section>
 <section id="price-elasticity" class="slide level2">
 <h2>Price Elasticity</h2>
-<p>The elasticity of a variable <span class="math inline">\(y(x, z)\)</span> with respect to another variable <span class="math inline">\(x\)</span> is defined as the percentage change in <span class="math inline">\(y\)</span> for a one percent change in <span class="math inline">\(x\)</span>. Mathematically, this is written as</p>
+<p>The elasticity of a variable <span class="math inline">\(y(x, z)\)</span> with respect to another variable <span class="math inline">\(x\)</span> is defined as the percentage change in <span class="math inline">\(y\)</span> for a <span class="math inline">\(1\%\)</span> change in <span class="math inline">\(x\)</span>. Mathematically, this is written as</p>
 <p><span class="math display">\[
 \eta = \frac{\partial \log(y(x, z))}{\partial \log(x)}
 \]</span></p>
@@ -512,8 +512,8 @@ <h2>Region Elasticities</h2>
 </figure>
 </div>
 </section>
-<section id="beline-model---unpooled" class="slide level2">
-<h2>Beline Model - Unpooled</h2>
+<section id="base-model---unpooled" class="slide level2">
+<h2>Base Model - Unpooled</h2>
 
 <img data-src="intro_hierarchical_models_files/images/unpooled_model.png" class="r-stretch quarto-figure-center"><p>We fit a linear model for each region <span class="math inline">\(j \in \{ 0,\cdots, 8 \}\)</span> separately:</p>
 <p><span class="math display">\[
@@ -523,8 +523,8 @@ <h2>Beline Model - Unpooled</h2>
 <p>See <a href="https://bayesiancomputationbook.com/markdown/chp_04.html#unpooled-parameters">Bayesian Modeling and Computation in Python, Chapter 4.5.1: Unpooled Parameters</a></p>
 </div>
 </section>
-<section id="beline-model---pooled-pymc" class="slide level2">
-<h2>Beline Model - Pooled (PyMC)</h2>
+<section id="base-model---pooled-pymc" class="slide level2">
+<h2>Base Model - Pooled (PyMC)</h2>
 <div class="sourceCode" id="cb1" data-code-line-numbers="|1|3|4-7|9-18|20-27"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1"></a>coords <span class="op">=</span> {<span class="st">"region"</span>: region, <span class="st">"obs"</span>: obs}</span>
 <span id="cb1-2"><a href="#cb1-2"></a></span>
 <span id="cb1-3"><a href="#cb1-3"></a><span class="cf">with</span> pm.Model(coords<span class="op">=</span>coords) <span class="im">as</span> base_model:</span>
@@ -553,8 +553,8 @@ <h2>Beline Model - Pooled (PyMC)</h2>
 <span id="cb1-26"><a href="#cb1-26"></a>        dims<span class="op">=</span><span class="st">"obs"</span></span>
 <span id="cb1-27"><a href="#cb1-27"></a>    )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </section>
-<section id="beline-model---pooled-plate-notation" class="slide level2">
-<h2>Beline Model - Pooled (Plate Notation)</h2>
+<section id="base-model---pooled-plate-notation" class="slide level2">
+<h2>Base Model - Pooled (Plate Notation)</h2>
 <div class="quarto-figure quarto-figure-center">
 <figure>
 <p><img data-src="intro_hierarchical_models_files/images/blog/multilevel_elasticities_single_sku_33_0.svg" height="600"></p>
@@ -648,15 +648,15 @@ <h2>Posterior Geometry Matters</h2>
 <p>See <a href="https://bayesiancomputationbook.com/markdown/chp_04.html#posterior-geometry-matters">Bayesian Modeling and Computation in Python, Chapter 4.6.1: Posterior Geometry Matters</a></p>
 </div>
 </section>
-<section id="non-ceneter-parametrization" class="slide level2 smaller">
-<h2>Non-Ceneter Parametrization</h2>
-<h3 id="ceneterd-parametrization">Ceneterd Parametrization</h3>
+<section id="non-centered-parametrization" class="slide level2 smaller">
+<h2>Non-Centered Parametrization</h2>
+<h3 id="centered-parametrization">Centered Parametrization</h3>
 <p><span class="math display">\[
 \begin{align*}
 \beta \sim \text{Normal}(\mu_{\beta}, \sigma_{\beta})
 \end{align*}
 \]</span></p>
-<h3 id="non-ceneter-parametrization-1">Non-Ceneter Parametrization</h3>
+<h3 id="non-centered-parametrization-1">Non-Centered Parametrization</h3>
 <p><span class="math display">\[
 \begin{align*}
 z \sim \text{Normal}(0, 1) \\

diff --git a/Presentations/intro_hierarchical_models/intro_hierarchical_models.qmd b/Presentations/intro_hierarchical_models/intro_hierarchical_models.qmd
@@ -37,7 +37,7 @@ format:
 - Models
     - Pooled
     - Hierarchical
-        - Non-Ceneter Parametrization
+        - Non-Centered Parametrization
         - Shrinkage Effect 
     - Hierarchical with Correlated Random Effects
 - Predictions for a New Region
@@ -80,7 +80,7 @@ format:
 ## Price Elasticity
 
 The elasticity of a variable $y(x, z)$ with respect to another variable $x$ is defined
-as the percentage change in $y$ for a one percent change in $x$.  Mathematically, this
+as the percentage change in $y$ for a $1\%$ change in $x$.  Mathematically, this
 is written as
 
 $$
@@ -121,7 +121,7 @@ Median income has an effect on price.
 
 ![](intro_hierarchical_models_files/images/blog/multilevel_elasticities_single_sku_25_2.png){fig-align="center" height="550"}
 
-## Beline Model - Unpooled
+## Base Model - Unpooled
 
 ![](intro_hierarchical_models_files/images/unpooled_model.png){fig-align="center"}
 
@@ -135,7 +135,7 @@ $$
 See [Bayesian Modeling and Computation in Python, Chapter 4.5.1: Unpooled Parameters](https://bayesiancomputationbook.com/markdown/chp_04.html#unpooled-parameters)
 :::
 
-## Beline Model - Pooled (PyMC)
+## Base Model - Pooled (PyMC)
 
 ``` {.python code-line-numbers="|1|3|4-7|9-18|20-27"}
 coords = {"region": region, "obs": obs}
@@ -167,7 +167,7 @@ with pm.Model(coords=coords) as base_model:
     )
 ```
 
-## Beline Model - Pooled (Plate Notation)
+## Base Model - Pooled (Plate Notation)
 
 ![](intro_hierarchical_models_files/images/blog/multilevel_elasticities_single_sku_33_0.svg){fig-align="center" height="600"}
 
@@ -255,17 +255,17 @@ See [Bayesian Modeling and Computation in Python, Chapter 4.6: Hierarchical Mode
 See [Bayesian Modeling and Computation in Python, Chapter 4.6.1: Posterior Geometry Matters](https://bayesiancomputationbook.com/markdown/chp_04.html#posterior-geometry-matters)
 :::
 
-## Non-Ceneter Parametrization {.smaller}
+## Non-Centered Parametrization {.smaller}
 
-### Ceneterd Parametrization
+### Centered Parametrization
 
 $$
 \begin{align*}
 \beta \sim \text{Normal}(\mu_{\beta}, \sigma_{\beta})
 \end{align*}
 $$
 
-### Non-Ceneter Parametrization
+### Non-Centered Parametrization
 
 $$
 \begin{align*}

diff --git a/Presentations/machine_learning_week_2023/revenue_retention_presentation.html b/Presentations/machine_learning_week_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-1.3.450">
+  <meta name="generator" content="quarto-1.3.353">
 
   <meta name="author" content="Dr.&nbsp;Juan Orduz">
   <title>Cohort Revenue &amp; Retention Analysis: A Bayesian Approach</title>
@@ -472,7 +472,7 @@ <h2>Some Bottom-Up Approaches</h2>
 </figure>
 </div>
 <ul>
-<li>Transacation process: <span class="math inline">\(\lambda \sim \text{Gamma}(r, \alpha)\)</span>.</li>
+<li>Transaction process: <span class="math inline">\(\lambda \sim \text{Gamma}(r, \alpha)\)</span>.</li>
 <li>Dropout probability: <span class="math inline">\(p \sim \text{Beta}(a, b)\)</span>.</li>
 </ul>
 </div>

diff --git a/Presentations/machine_learning_week_2023/revenue_retention_presentation.qmd b/Presentations/machine_learning_week_2023/revenue_retention_presentation.qmd
@@ -99,7 +99,7 @@ format:
 
 ![](revenue_retention_presentation_files/images/revenue_retention_presentation_files/p_alive.png){fig-align="center"}
 
-- Transacation process: $\lambda \sim \text{Gamma}(r, \alpha)$.
+- Transaction process: $\lambda \sim \text{Gamma}(r, \alpha)$.
 - Dropout probability: $p \sim \text{Beta}(a, b)$. 
 
 :::