From fe633889ab691899d1c26bad2386350bb7125a59 Mon Sep 17 00:00:00 2001
From: Morten Hjorth-Jensen <morten.hjorth-jensen@fys.uio.no>
Date: Thu, 23 Sep 2021 10:49:48 +0200
Subject: [PATCH] typo rinsing

---
 doc/pub/week38/html/._week38-bs005.html      |   2 +-
 doc/pub/week38/html/week38-reveal.html       |   2 +-
 doc/pub/week38/html/week38-solarized.html    |   2 +-
 doc/pub/week38/html/week38.html              |   2 +-
 doc/pub/week38/ipynb/ipynb-week38-src.tar.gz | Bin 192 -> 193 bytes
 doc/pub/week38/ipynb/week38.ipynb            |   2 +-
 doc/src/week38/week38.do.txt                 |   2 +-
 7 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/doc/pub/week38/html/._week38-bs005.html b/doc/pub/week38/html/._week38-bs005.html
index 5666274a4..f5b65e226 100644
--- a/doc/pub/week38/html/._week38-bs005.html
+++ b/doc/pub/week38/html/._week38-bs005.html
@@ -436,7 +436,7 @@ $$
 
 
 <ul>
-<li> Evaluate the prediction performance of these models on the test set by \( \log\{C[y_i, \boldsymbol{X}_{i, \ast}; \boldsymbol{\beta}_{-i}(\lambda), \boldsymbol{\sigma}_{-i}^2(\lambda)]\} \). Or, by the prediction error \( |y_i - \boldsymbol{X}_{i, \ast} \boldsymbol{\beta}_{-i}(\lambda)| \), the relative error, the error squared or the R2 score function.</li>
+<li> Evaluate the prediction performance of these models on the test set by \( C[y_i, \boldsymbol{X}_{i, \ast}; \boldsymbol{\beta}_{-i}(\lambda), \boldsymbol{\sigma}_{-i}^2(\lambda)] \). Or, by the prediction error \( |y_i - \boldsymbol{X}_{i, \ast} \boldsymbol{\beta}_{-i}(\lambda)| \), the relative error, the error squared or the R2 score function.</li>
 <li> Repeat the first three steps  such that each sample plays the role of the test set once.</li>
 <li> Average the prediction performances of the test sets at each grid point of the penalty bias/parameter. It is an estimate of the prediction performance of the model corresponding to this value of the penalty parameter on novel data.</li> 
 </ul>
diff --git a/doc/pub/week38/html/week38-reveal.html b/doc/pub/week38/html/week38-reveal.html
index 5a8c2fd26..47d2e6b21 100644
--- a/doc/pub/week38/html/week38-reveal.html
+++ b/doc/pub/week38/html/week38-reveal.html
@@ -286,7 +286,7 @@ $$
 
 
 <ul>
-<p><li> Evaluate the prediction performance of these models on the test set by \( \log\{C[y_i, \boldsymbol{X}_{i, \ast}; \boldsymbol{\beta}_{-i}(\lambda), \boldsymbol{\sigma}_{-i}^2(\lambda)]\} \). Or, by the prediction error \( |y_i - \boldsymbol{X}_{i, \ast} \boldsymbol{\beta}_{-i}(\lambda)| \), the relative error, the error squared or the R2 score function.</li>
+<p><li> Evaluate the prediction performance of these models on the test set by \( C[y_i, \boldsymbol{X}_{i, \ast}; \boldsymbol{\beta}_{-i}(\lambda), \boldsymbol{\sigma}_{-i}^2(\lambda)] \). Or, by the prediction error \( |y_i - \boldsymbol{X}_{i, \ast} \boldsymbol{\beta}_{-i}(\lambda)| \), the relative error, the error squared or the R2 score function.</li>
 <p><li> Repeat the first three steps  such that each sample plays the role of the test set once.</li>
 <p><li> Average the prediction performances of the test sets at each grid point of the penalty bias/parameter. It is an estimate of the prediction performance of the model corresponding to this value of the penalty parameter on novel data.</li> 
 </ul>
diff --git a/doc/pub/week38/html/week38-solarized.html b/doc/pub/week38/html/week38-solarized.html
index e9c3e56ea..c55e90506 100644
--- a/doc/pub/week38/html/week38-solarized.html
+++ b/doc/pub/week38/html/week38-solarized.html
@@ -453,7 +453,7 @@ $$
 
 
 <ul>
-<li> Evaluate the prediction performance of these models on the test set by \( \log\{C[y_i, \boldsymbol{X}_{i, \ast}; \boldsymbol{\beta}_{-i}(\lambda), \boldsymbol{\sigma}_{-i}^2(\lambda)]\} \). Or, by the prediction error \( |y_i - \boldsymbol{X}_{i, \ast} \boldsymbol{\beta}_{-i}(\lambda)| \), the relative error, the error squared or the R2 score function.</li>
+<li> Evaluate the prediction performance of these models on the test set by \( C[y_i, \boldsymbol{X}_{i, \ast}; \boldsymbol{\beta}_{-i}(\lambda), \boldsymbol{\sigma}_{-i}^2(\lambda)] \). Or, by the prediction error \( |y_i - \boldsymbol{X}_{i, \ast} \boldsymbol{\beta}_{-i}(\lambda)| \), the relative error, the error squared or the R2 score function.</li>
 <li> Repeat the first three steps  such that each sample plays the role of the test set once.</li>
 <li> Average the prediction performances of the test sets at each grid point of the penalty bias/parameter. It is an estimate of the prediction performance of the model corresponding to this value of the penalty parameter on novel data.</li> 
 </ul>
diff --git a/doc/pub/week38/html/week38.html b/doc/pub/week38/html/week38.html
index 6e6838e87..0eb03ac64 100644
--- a/doc/pub/week38/html/week38.html
+++ b/doc/pub/week38/html/week38.html
@@ -458,7 +458,7 @@ $$
 
 
 <ul>
-<li> Evaluate the prediction performance of these models on the test set by \( \log\{C[y_i, \boldsymbol{X}_{i, \ast}; \boldsymbol{\beta}_{-i}(\lambda), \boldsymbol{\sigma}_{-i}^2(\lambda)]\} \). Or, by the prediction error \( |y_i - \boldsymbol{X}_{i, \ast} \boldsymbol{\beta}_{-i}(\lambda)| \), the relative error, the error squared or the R2 score function.</li>
+<li> Evaluate the prediction performance of these models on the test set by \( C[y_i, \boldsymbol{X}_{i, \ast}; \boldsymbol{\beta}_{-i}(\lambda), \boldsymbol{\sigma}_{-i}^2(\lambda)] \). Or, by the prediction error \( |y_i - \boldsymbol{X}_{i, \ast} \boldsymbol{\beta}_{-i}(\lambda)| \), the relative error, the error squared or the R2 score function.</li>
 <li> Repeat the first three steps  such that each sample plays the role of the test set once.</li>
 <li> Average the prediction performances of the test sets at each grid point of the penalty bias/parameter. It is an estimate of the prediction performance of the model corresponding to this value of the penalty parameter on novel data.</li> 
 </ul>
diff --git a/doc/pub/week38/ipynb/ipynb-week38-src.tar.gz b/doc/pub/week38/ipynb/ipynb-week38-src.tar.gz
index 9849d0483c54e5007b98904394bd1a7a35201524..f2ebad6078f2a7665ca6e088504d117304a14ca6 100644
GIT binary patch
delta 117
zcmV-*0E+*>0l@(UABzY8osk7qXToA2l*M7{0a@;(m(EzOhf|vBjMAWdH#dxx<%d1<
zEAY%eajb-a?Y_5_k|34ET&Wsvh&5XUqU~i4g+ep7K;yL&8iC6mcu@!|l;jt`)#{{q
XV*>xz&p3|bIA41JaO88`00;m8!~r)L

delta 116
zcmV-)0E_>@0l)zTABzY8L6HSjW=tp$%3_*&K$bh{r8Ac6;gqI2qckYr%?)E^`C-re
z3Ow^q94ld9yYFqKBuHg3SE_~^V$D{8XnUDMq0o#i(0J{HM&PmsUKGL#CHci~wK{3u
Wn85$_Gmhgp&etA)6TO502mk<^i!|W?

diff --git a/doc/pub/week38/ipynb/week38.ipynb b/doc/pub/week38/ipynb/week38.ipynb
index 40483a026..ee1a91390 100644
--- a/doc/pub/week38/ipynb/week38.ipynb
+++ b/doc/pub/week38/ipynb/week38.ipynb
@@ -199,7 +199,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "* Evaluate the prediction performance of these models on the test set by $\\log\\{C[y_i, \\boldsymbol{X}_{i, \\ast}; \\boldsymbol{\\beta}_{-i}(\\lambda), \\boldsymbol{\\sigma}_{-i}^2(\\lambda)]\\}$. Or, by the prediction error $|y_i - \\boldsymbol{X}_{i, \\ast} \\boldsymbol{\\beta}_{-i}(\\lambda)|$, the relative error, the error squared or the R2 score function.\n",
+    "* Evaluate the prediction performance of these models on the test set by $C[y_i, \\boldsymbol{X}_{i, \\ast}; \\boldsymbol{\\beta}_{-i}(\\lambda), \\boldsymbol{\\sigma}_{-i}^2(\\lambda)]$. Or, by the prediction error $|y_i - \\boldsymbol{X}_{i, \\ast} \\boldsymbol{\\beta}_{-i}(\\lambda)|$, the relative error, the error squared or the R2 score function.\n",
     "\n",
     "* Repeat the first three steps  such that each sample plays the role of the test set once.\n",
     "\n",
diff --git a/doc/src/week38/week38.do.txt b/doc/src/week38/week38.do.txt
index 64769edf5..c0d0b7d77 100644
--- a/doc/src/week38/week38.do.txt
+++ b/doc/src/week38/week38.do.txt
@@ -115,7 +115,7 @@ cross-validation (LOOCV).
 \end{align*}
 !et 
 
-* Evaluate the prediction performance of these models on the test set by $\log\{C[y_i, \bm{X}_{i, \ast}; \bm{\beta}_{-i}(\lambda), \bm{\sigma}_{-i}^2(\lambda)]\}$. Or, by the prediction error $|y_i - \bm{X}_{i, \ast} \bm{\beta}_{-i}(\lambda)|$, the relative error, the error squared or the R2 score function.
+* Evaluate the prediction performance of these models on the test set by $C[y_i, \bm{X}_{i, \ast}; \bm{\beta}_{-i}(\lambda), \bm{\sigma}_{-i}^2(\lambda)]$. Or, by the prediction error $|y_i - \bm{X}_{i, \ast} \bm{\beta}_{-i}(\lambda)|$, the relative error, the error squared or the R2 score function.
 
 * Repeat the first three steps  such that each sample plays the role of the test set once.