From 6a08df1fba0b96de0ef2cc5223c1cd7943c3e6d3 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Mon, 5 Sep 2022 21:35:25 +0200 Subject: [PATCH] typos in week 35 --- doc/pub/week35/html/._week35-bs000.html | 2 +- doc/pub/week35/html/._week35-bs050.html | 10 +- doc/pub/week35/html/week35-bs.html | 2 +- doc/pub/week35/html/week35-reveal.html | 14 +- doc/pub/week35/html/week35-solarized.html | 12 +- doc/pub/week35/html/week35.html | 12 +- doc/pub/week35/ipynb/ipynb-week35-src.tar.gz | Bin 191 -> 192 bytes doc/pub/week35/ipynb/week35.ipynb | 858 +++++++++---------- doc/src/week35/week35.do.txt | 11 +- 9 files changed, 433 insertions(+), 488 deletions(-) diff --git a/doc/pub/week35/html/._week35-bs000.html b/doc/pub/week35/html/._week35-bs000.html index 6c5f4a560..edf5fd391 100644 --- a/doc/pub/week35/html/._week35-bs000.html +++ b/doc/pub/week35/html/._week35-bs000.html @@ -404,7 +404,7 @@ MathJax.Hub.Config({
-

Sep 2, 2022

+

Sep 5, 2022


diff --git a/doc/pub/week35/html/._week35-bs050.html b/doc/pub/week35/html/._week35-bs050.html index e082f1b15..c39729458 100644 --- a/doc/pub/week35/html/._week35-bs050.html +++ b/doc/pub/week35/html/._week35-bs050.html @@ -402,12 +402,6 @@ $$

We define \( \boldsymbol{\Sigma}^T\boldsymbol{\Sigma}=\tilde{\boldsymbol{\Sigma}}^2 \) which is a diagonal matrix containing only the singular values squared. It has dimensionality \( p \times p \).

-

This means, using the orthogonality of \( \boldsymbol{V} \), that we get

- -$$ -\boldsymbol{X}^T\boldsymbol{X}=\tilde{\boldsymbol{\Sigma}}^2. -$$ -

We can now insert the result for the matrix \( \boldsymbol{X}^T\boldsymbol{X} \) into our equation for ordinary least squares where

$$ @@ -417,10 +411,10 @@ $$

and using our SVD decomposition of \( \boldsymbol{X} \) we have

$$ -\tilde{y}_{\mathrm{OLS}}=\boldsymbol{U}\boldsymbol{\Sigma}\boldsymbol{V}^T\tilde{\boldsymbol{\Sigma}}^{-2}\boldsymbol{V}\boldsymbol{\Sigma}^T\boldsymbol{U}^T\boldsymbol{y}, +\tilde{y}_{\mathrm{OLS}}=\boldsymbol{U}\boldsymbol{\Sigma}\boldsymbol{V}^T\left(\boldsymbol{V}\tilde{\boldsymbol{\Sigma}}^{2}(\boldsymbol{V}^T\right)^{-1}\boldsymbol{V}\boldsymbol{\Sigma}^T\boldsymbol{U}^T\boldsymbol{y}, $$ -

which gives us, using the orthogonality of the matrices \( \boldsymbol{U} \) and \( \boldsymbol{V} \),

+

which gives us, using the orthogonality of the matrices \( \boldsymbol{U} \) and \( \boldsymbol{V} \),,

$$ \tilde{y}_{\mathrm{OLS}}=\boldsymbol{U}\boldsymbol{U}^T\boldsymbol{y}=\sum_{i=0}^{p-1}\boldsymbol{u}_i\boldsymbol{u}^T_j\boldsymbol{y}, diff --git a/doc/pub/week35/html/week35-bs.html b/doc/pub/week35/html/week35-bs.html index 6c5f4a560..edf5fd391 100644 --- a/doc/pub/week35/html/week35-bs.html +++ b/doc/pub/week35/html/week35-bs.html @@ -404,7 +404,7 @@ MathJax.Hub.Config({
-

Sep 2, 2022

+

Sep 5, 2022


diff --git a/doc/pub/week35/html/week35-reveal.html b/doc/pub/week35/html/week35-reveal.html index cff9aa38e..a879fdbeb 100644 --- a/doc/pub/week35/html/week35-reveal.html +++ b/doc/pub/week35/html/week35-reveal.html @@ -184,7 +184,7 @@ MathJax.Hub.Config({
-

Sep 2, 2022

+

Sep 5, 2022


@@ -2538,14 +2538,6 @@ $$

We define \( \boldsymbol{\Sigma}^T\boldsymbol{\Sigma}=\tilde{\boldsymbol{\Sigma}}^2 \) which is a diagonal matrix containing only the singular values squared. It has dimensionality \( p \times p \).

-

This means, using the orthogonality of \( \boldsymbol{V} \), that we get

- -

 
-$$ -\boldsymbol{X}^T\boldsymbol{X}=\tilde{\boldsymbol{\Sigma}}^2. -$$ -

 
-

We can now insert the result for the matrix \( \boldsymbol{X}^T\boldsymbol{X} \) into our equation for ordinary least squares where

 
@@ -2558,11 +2550,11 @@ $$

 
$$ -\tilde{y}_{\mathrm{OLS}}=\boldsymbol{U}\boldsymbol{\Sigma}\boldsymbol{V}^T\tilde{\boldsymbol{\Sigma}}^{-2}\boldsymbol{V}\boldsymbol{\Sigma}^T\boldsymbol{U}^T\boldsymbol{y}, +\tilde{y}_{\mathrm{OLS}}=\boldsymbol{U}\boldsymbol{\Sigma}\boldsymbol{V}^T\left(\boldsymbol{V}\tilde{\boldsymbol{\Sigma}}^{2}(\boldsymbol{V}^T\right)^{-1}\boldsymbol{V}\boldsymbol{\Sigma}^T\boldsymbol{U}^T\boldsymbol{y}, $$

 
-

which gives us, using the orthogonality of the matrices \( \boldsymbol{U} \) and \( \boldsymbol{V} \),

+

which gives us, using the orthogonality of the matrices \( \boldsymbol{U} \) and \( \boldsymbol{V} \),,

 
$$ diff --git a/doc/pub/week35/html/week35-solarized.html b/doc/pub/week35/html/week35-solarized.html index d493bcd33..9b0c076b4 100644 --- a/doc/pub/week35/html/week35-solarized.html +++ b/doc/pub/week35/html/week35-solarized.html @@ -330,7 +330,7 @@ MathJax.Hub.Config({

-

Sep 2, 2022

+

Sep 5, 2022


@@ -2513,12 +2513,6 @@ $$

We define \( \boldsymbol{\Sigma}^T\boldsymbol{\Sigma}=\tilde{\boldsymbol{\Sigma}}^2 \) which is a diagonal matrix containing only the singular values squared. It has dimensionality \( p \times p \).

-

This means, using the orthogonality of \( \boldsymbol{V} \), that we get

- -$$ -\boldsymbol{X}^T\boldsymbol{X}=\tilde{\boldsymbol{\Sigma}}^2. -$$ -

We can now insert the result for the matrix \( \boldsymbol{X}^T\boldsymbol{X} \) into our equation for ordinary least squares where

$$ @@ -2528,10 +2522,10 @@ $$

and using our SVD decomposition of \( \boldsymbol{X} \) we have

$$ -\tilde{y}_{\mathrm{OLS}}=\boldsymbol{U}\boldsymbol{\Sigma}\boldsymbol{V}^T\tilde{\boldsymbol{\Sigma}}^{-2}\boldsymbol{V}\boldsymbol{\Sigma}^T\boldsymbol{U}^T\boldsymbol{y}, +\tilde{y}_{\mathrm{OLS}}=\boldsymbol{U}\boldsymbol{\Sigma}\boldsymbol{V}^T\left(\boldsymbol{V}\tilde{\boldsymbol{\Sigma}}^{2}(\boldsymbol{V}^T\right)^{-1}\boldsymbol{V}\boldsymbol{\Sigma}^T\boldsymbol{U}^T\boldsymbol{y}, $$ -

which gives us, using the orthogonality of the matrices \( \boldsymbol{U} \) and \( \boldsymbol{V} \),

+

which gives us, using the orthogonality of the matrices \( \boldsymbol{U} \) and \( \boldsymbol{V} \),,

$$ \tilde{y}_{\mathrm{OLS}}=\boldsymbol{U}\boldsymbol{U}^T\boldsymbol{y}=\sum_{i=0}^{p-1}\boldsymbol{u}_i\boldsymbol{u}^T_j\boldsymbol{y}, diff --git a/doc/pub/week35/html/week35.html b/doc/pub/week35/html/week35.html index f3d317134..36d50bc9a 100644 --- a/doc/pub/week35/html/week35.html +++ b/doc/pub/week35/html/week35.html @@ -407,7 +407,7 @@ MathJax.Hub.Config({
-

Sep 2, 2022

+

Sep 5, 2022


@@ -2590,12 +2590,6 @@ $$

We define \( \boldsymbol{\Sigma}^T\boldsymbol{\Sigma}=\tilde{\boldsymbol{\Sigma}}^2 \) which is a diagonal matrix containing only the singular values squared. It has dimensionality \( p \times p \).

-

This means, using the orthogonality of \( \boldsymbol{V} \), that we get

- -$$ -\boldsymbol{X}^T\boldsymbol{X}=\tilde{\boldsymbol{\Sigma}}^2. -$$ -

We can now insert the result for the matrix \( \boldsymbol{X}^T\boldsymbol{X} \) into our equation for ordinary least squares where

$$ @@ -2605,10 +2599,10 @@ $$

and using our SVD decomposition of \( \boldsymbol{X} \) we have

$$ -\tilde{y}_{\mathrm{OLS}}=\boldsymbol{U}\boldsymbol{\Sigma}\boldsymbol{V}^T\tilde{\boldsymbol{\Sigma}}^{-2}\boldsymbol{V}\boldsymbol{\Sigma}^T\boldsymbol{U}^T\boldsymbol{y}, +\tilde{y}_{\mathrm{OLS}}=\boldsymbol{U}\boldsymbol{\Sigma}\boldsymbol{V}^T\left(\boldsymbol{V}\tilde{\boldsymbol{\Sigma}}^{2}(\boldsymbol{V}^T\right)^{-1}\boldsymbol{V}\boldsymbol{\Sigma}^T\boldsymbol{U}^T\boldsymbol{y}, $$ -

which gives us, using the orthogonality of the matrices \( \boldsymbol{U} \) and \( \boldsymbol{V} \),

+

which gives us, using the orthogonality of the matrices \( \boldsymbol{U} \) and \( \boldsymbol{V} \),,

$$ \tilde{y}_{\mathrm{OLS}}=\boldsymbol{U}\boldsymbol{U}^T\boldsymbol{y}=\sum_{i=0}^{p-1}\boldsymbol{u}_i\boldsymbol{u}^T_j\boldsymbol{y}, diff --git a/doc/pub/week35/ipynb/ipynb-week35-src.tar.gz b/doc/pub/week35/ipynb/ipynb-week35-src.tar.gz index 67da7d35dbb3d9ca46cd7bd1701c51dc64ed4a05..c88901ef6c4591bdbc96696d2b22e91a22562a9b 100644 GIT binary patch delta 150 zcmV;H0BQff0l)zzABzY8a8DLv00ZsM%?iRW3gilFUb3KV;4l$#IGnVp%0EsE5Gyt;PNiUss!VIT0)ftsVk!~0{hq=}@ z+z@NZBiZ&UheD$rTVU|oNrNC%527fflUj*O*cyE@qOno<>t{UA^SrM;0BI_@M*s)_ E0Lz9xIsgCw delta 149 zcmV;G0BZlh0lxtyABzY8cm@(<00ZsM%?iRW3hq{-q