Built site for gh-pages

.nojekyll

@@ -1 +1 @@
-9b7cccd9
+a767f8d4

P0C0_notation.html

@@ -429,7 +429,7 @@ <h2 class="anchored" data-anchor-id="fonts-matrices-vectors">Fonts, matrices, ve
 <h2 class="anchored" data-anchor-id="functions">Functions</h2>
 <p>Typically, a ‘hat’, <span class="math inline">\(\hat{x}\)</span>, will refer to the prediction or estimation of a variable, <span class="math inline">\(x\)</span>, with bold-face used again to represent vectors. A ‘bar’, <span class="math inline">\(\bar{x}\)</span>, refers to the sample mean of <span class="math inline">\(\mathbf{x}\)</span>. Capital letters in normal font, <span class="math inline">\(X\)</span>, refer to scalar or vector random variables, which will be made clear from context. <span class="math inline">\(\mathbb{E}(X)\)</span> is the expectation of the random variable <span class="math inline">\(X\)</span>. We write <span class="math inline">\(A \perp \!\!\! \perp B\)</span>, to denote that <span class="math inline">\(A\)</span> and <span class="math inline">\(B\)</span> are independent, i.e., that <span class="math inline">\(P(A \cap B) = P(A)P(B)\)</span>.</p>
 <p>A function <span class="math inline">\(f\)</span>, will either be written as a formal map of domain to codomain, <span class="math inline">\(f: \mathcal{X}\rightarrow \mathcal{Y}; (x, y) \mapsto f(x, y)\)</span> (which is most useful for understanding inputs and outputs), or more simply and commonly as <span class="math inline">\(f(x, y)\)</span>. Given a random variable, <span class="math inline">\(X\)</span>, following distribution <span class="math inline">\(\zeta\)</span> (mathematically written <span class="math inline">\(X \sim \zeta\)</span>), then <span class="math inline">\(f_X\)</span> denotes the probability density function, and analogously for other distribution defining functions such as the cumulative distribution function, survival function, etc. In the survival analysis context (<a href="P1C4_survival.html" class="quarto-xref"><span>4&nbsp; Survival Analysis</span></a>), a subscript “<span class="math inline">\(0\)</span>” refers to a “baseline” function, for example, <span class="math inline">\(S_0\)</span> is the baseline survival function.</p>
-<p>Finally <span class="math inline">\(\exp\)</span> refers to the exponential function and <span class="math inline">\(\log\)</span> refers to the natural logarithm <span class="math inline">\(\ln(x) = \log_e(x)\)</span>.</p>
+<p>Finally, <span class="math inline">\(\exp\)</span>, refers to the exponential function, <span class="math inline">\(f(x) = e^x\)</span>, and <span class="math inline">\(\log\)</span> refers to the natural logarithm <span class="math inline">\(\ln(x) = \log_e(x)\)</span>.</p>
 </section>
 <section id="variables-and-acronyms" class="level2">
 <h2 class="anchored" data-anchor-id="variables-and-acronyms">Variables and acronyms</h2>

search.json

@@ -84,7 +84,7 @@
     "href": "P0C0_notation.html#functions",
     "title": "Symbols and Notation",
     "section": "Functions",
-    "text": "Functions\nTypically, a ‘hat’, \\(\\hat{x}\\), will refer to the prediction or estimation of a variable, \\(x\\), with bold-face used again to represent vectors. A ‘bar’, \\(\\bar{x}\\), refers to the sample mean of \\(\\mathbf{x}\\). Capital letters in normal font, \\(X\\), refer to scalar or vector random variables, which will be made clear from context. \\(\\mathbb{E}(X)\\) is the expectation of the random variable \\(X\\). We write \\(A \\perp \\!\\!\\! \\perp B\\), to denote that \\(A\\) and \\(B\\) are independent, i.e., that \\(P(A \\cap B) = P(A)P(B)\\).\nA function \\(f\\), will either be written as a formal map of domain to codomain, \\(f: \\mathcal{X}\\rightarrow \\mathcal{Y}; (x, y) \\mapsto f(x, y)\\) (which is most useful for understanding inputs and outputs), or more simply and commonly as \\(f(x, y)\\). Given a random variable, \\(X\\), following distribution \\(\\zeta\\) (mathematically written \\(X \\sim \\zeta\\)), then \\(f_X\\) denotes the probability density function, and analogously for other distribution defining functions such as the cumulative distribution function, survival function, etc. In the survival analysis context (4  Survival Analysis), a subscript “\\(0\\)” refers to a “baseline” function, for example, \\(S_0\\) is the baseline survival function.\nFinally \\(\\exp\\) refers to the exponential function and \\(\\log\\) refers to the natural logarithm \\(\\ln(x) = \\log_e(x)\\).",
+    "text": "Functions\nTypically, a ‘hat’, \\(\\hat{x}\\), will refer to the prediction or estimation of a variable, \\(x\\), with bold-face used again to represent vectors. A ‘bar’, \\(\\bar{x}\\), refers to the sample mean of \\(\\mathbf{x}\\). Capital letters in normal font, \\(X\\), refer to scalar or vector random variables, which will be made clear from context. \\(\\mathbb{E}(X)\\) is the expectation of the random variable \\(X\\). We write \\(A \\perp \\!\\!\\! \\perp B\\), to denote that \\(A\\) and \\(B\\) are independent, i.e., that \\(P(A \\cap B) = P(A)P(B)\\).\nA function \\(f\\), will either be written as a formal map of domain to codomain, \\(f: \\mathcal{X}\\rightarrow \\mathcal{Y}; (x, y) \\mapsto f(x, y)\\) (which is most useful for understanding inputs and outputs), or more simply and commonly as \\(f(x, y)\\). Given a random variable, \\(X\\), following distribution \\(\\zeta\\) (mathematically written \\(X \\sim \\zeta\\)), then \\(f_X\\) denotes the probability density function, and analogously for other distribution defining functions such as the cumulative distribution function, survival function, etc. In the survival analysis context (4  Survival Analysis), a subscript “\\(0\\)” refers to a “baseline” function, for example, \\(S_0\\) is the baseline survival function.\nFinally, \\(\\exp\\), refers to the exponential function, \\(f(x) = e^x\\), and \\(\\log\\) refers to the natural logarithm \\(\\ln(x) = \\log_e(x)\\).",
     "crumbs": [
       "Symbols and Notation"
     ]