diff --git a/doc/pub/week36/html/week36-bs.html b/doc/pub/week36/html/week36-bs.html
index 67a426766..11aacc49a 100644
--- a/doc/pub/week36/html/week36-bs.html
+++ b/doc/pub/week36/html/week36-bs.html
@@ -106,6 +106,10 @@ Automatically generated HTML file from DocOnce source
2,
None,
'deriving-ols-from-a-probability-distribution'),
+ ('Independent and Identically Distrubuted (iid)',
+ 2,
+ None,
+ 'independent-and-identically-distrubuted-iid'),
('Friday September 10', 2, None, 'friday-september-10'),
('Why resampling methods', 2, None, 'why-resampling-methods'),
('Resampling methods', 2, None, 'resampling-methods'),
@@ -256,34 +260,35 @@ MathJax.Hub.Config({
Expectation value and variance
Expectation value and variance for \( \boldsymbol{\beta} \)
Deriving OLS from a probability distribution
- Friday September 10
- Why resampling methods
- Resampling methods
- Resampling approaches can be computationally expensive
- Why resampling methods ?
- Statistical analysis
- Resampling methods
- Resampling methods: Jackknife and Bootstrap
- Resampling methods: Jackknife
- Jackknife code example
- Resampling methods: Bootstrap
- Resampling methods: Bootstrap background
- Resampling methods: More Bootstrap background
- Resampling methods: Bootstrap approach
- Resampling methods: Bootstrap steps
- Code example for the Bootstrap method
- Various steps in cross-validation
- How to set up the cross-validation for Ridge and/or Lasso
- Cross-validation in brief
- Code Example for Cross-validation and \( k \)-fold Cross-validation
- The bias-variance tradeoff
- Example code for Bias-Variance tradeoff
- Understanding what happens
- Summing up
- Another Example from Scikit-Learn's Repository
- More examples on bootstrap and cross-validation and errors
- The same example but now with cross-validation
- Cross-validation with Ridge
+ Independent and Identically Distrubuted (iid)
+ Friday September 10
+ Why resampling methods
+ Resampling methods
+ Resampling approaches can be computationally expensive
+ Why resampling methods ?
+ Statistical analysis
+ Resampling methods
+ Resampling methods: Jackknife and Bootstrap
+ Resampling methods: Jackknife
+ Jackknife code example
+ Resampling methods: Bootstrap
+ Resampling methods: Bootstrap background
+ Resampling methods: More Bootstrap background
+ Resampling methods: Bootstrap approach
+ Resampling methods: Bootstrap steps
+ Code example for the Bootstrap method
+ Various steps in cross-validation
+ How to set up the cross-validation for Ridge and/or Lasso
+ Cross-validation in brief
+ Code Example for Cross-validation and \( k \)-fold Cross-validation
+ The bias-variance tradeoff
+ Example code for Bias-Variance tradeoff
+ Understanding what happens
+ Summing up
+ Another Example from Scikit-Learn's Repository
+ More examples on bootstrap and cross-validation and errors
+ The same example but now with cross-validation
+ Cross-validation with Ridge
@@ -342,7 +347,7 @@ MathJax.Hub.Config({
9
10
...
- 53
+ 54
»
diff --git a/doc/pub/week36/html/week36-reveal.html b/doc/pub/week36/html/week36-reveal.html
index 4b0f3d4d0..d5e64189d 100644
--- a/doc/pub/week36/html/week36-reveal.html
+++ b/doc/pub/week36/html/week36-reveal.html
@@ -956,6 +956,20 @@ the design matrix are not stocastic variables, we can assume that the
probability distribution of our targets is also a normal distribution
but now with mean value \( \boldsymbol{X}\hat{\boldsymbol{\beta}} \). This means that a
single output \( y_i \) is given by the Gaussian distribution
+
+
+$$
+y_i\sim \mathcal{N}(\boldsymbol{X}{i,*}\boldsymbol{\beta}, \sigma^2)=\frac{1}{\sqrt{2\pi\sigma^2}}\exp{\left[-\frac{(y_i-\boldsymbol{X}{i,*}\boldsymbol{\beta})^2}{2\sigma^2}\right]}.
+$$
+
+
+
+
+
+Independent and Identically Distrubuted (iid)
+
+
+We assume now that the various \( y_i \) values are stochastically distributed according to the above Gaussian distribution and
diff --git a/doc/pub/week36/html/week36-solarized.html b/doc/pub/week36/html/week36-solarized.html
index 063fa2ea7..1bccf3b26 100644
--- a/doc/pub/week36/html/week36-solarized.html
+++ b/doc/pub/week36/html/week36-solarized.html
@@ -126,6 +126,10 @@ div { text-align: justify; text-justify: inter-word; }
2,
None,
'deriving-ols-from-a-probability-distribution'),
+ ('Independent and Identically Distrubuted (iid)',
+ 2,
+ None,
+ 'independent-and-identically-distrubuted-iid'),
('Friday September 10', 2, None, 'friday-september-10'),
('Why resampling methods', 2, None, 'why-resampling-methods'),
('Resampling methods', 2, None, 'resampling-methods'),
@@ -948,6 +952,18 @@ probability distribution of our targets is also a normal distribution
but now with mean value \( \boldsymbol{X}\hat{\boldsymbol{\beta}} \). This means that a
single output \( y_i \) is given by the Gaussian distribution
+$$
+y_i\sim \mathcal{N}(\boldsymbol{X}{i,*}\boldsymbol{\beta}, \sigma^2)=\frac{1}{\sqrt{2\pi\sigma^2}}\exp{\left[-\frac{(y_i-\boldsymbol{X}{i,*}\boldsymbol{\beta})^2}{2\sigma^2}\right]}.
+$$
+
+
+
+
+
Independent and Identically Distrubuted (iid)
+
+
+We assume now that the various \( y_i \) values are stochastically distributed according to the above Gaussian distribution and
+
diff --git a/doc/pub/week36/html/week36.html b/doc/pub/week36/html/week36.html
index 58b376813..fabda1bf3 100644
--- a/doc/pub/week36/html/week36.html
+++ b/doc/pub/week36/html/week36.html
@@ -131,6 +131,10 @@ div { text-align: justify; text-justify: inter-word; }
2,
None,
'deriving-ols-from-a-probability-distribution'),
+ ('Independent and Identically Distrubuted (iid)',
+ 2,
+ None,
+ 'independent-and-identically-distrubuted-iid'),
('Friday September 10', 2, None, 'friday-september-10'),
('Why resampling methods', 2, None, 'why-resampling-methods'),
('Resampling methods', 2, None, 'resampling-methods'),
@@ -953,6 +957,18 @@ probability distribution of our targets is also a normal distribution
but now with mean value \( \boldsymbol{X}\hat{\boldsymbol{\beta}} \). This means that a
single output \( y_i \) is given by the Gaussian distribution
+$$
+y_i\sim \mathcal{N}(\boldsymbol{X}{i,*}\boldsymbol{\beta}, \sigma^2)=\frac{1}{\sqrt{2\pi\sigma^2}}\exp{\left[-\frac{(y_i-\boldsymbol{X}{i,*}\boldsymbol{\beta})^2}{2\sigma^2}\right]}.
+$$
+
+
+
+
+
Independent and Identically Distrubuted (iid)
+
+
+We assume now that the various \( y_i \) values are stochastically distributed according to the above Gaussian distribution and
+
diff --git a/doc/pub/week36/ipynb/ipynb-week36-src.tar.gz b/doc/pub/week36/ipynb/ipynb-week36-src.tar.gz
index 0d8bee72a..fae1ac5a5 100644
Binary files a/doc/pub/week36/ipynb/ipynb-week36-src.tar.gz and b/doc/pub/week36/ipynb/ipynb-week36-src.tar.gz differ
diff --git a/doc/pub/week36/ipynb/week36.ipynb b/doc/pub/week36/ipynb/week36.ipynb
index 70e4885f7..2c0731daa 100644
--- a/doc/pub/week36/ipynb/week36.ipynb
+++ b/doc/pub/week36/ipynb/week36.ipynb
@@ -1170,9 +1170,25 @@
"the design matrix are not stocastic variables, we can assume that the\n",
"probability distribution of our targets is also a normal distribution\n",
"but now with mean value $\\boldsymbol{X}\\hat{\\boldsymbol{\\beta}}$. This means that a\n",
- "single output $y_i$ is given by the Gaussian distribution\n",
- "\n",
+ "single output $y_i$ is given by the Gaussian distribution"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$\n",
+ "y_i\\sim \\mathcal{N}(\\boldsymbol{X}{i,*}\\boldsymbol{\\beta}, \\sigma^2)=\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp{\\left[-\\frac{(y_i-\\boldsymbol{X}{i,*}\\boldsymbol{\\beta})^2}{2\\sigma^2}\\right]}.\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Independent and Identically Distrubuted (iid)\n",
"\n",
+ "We assume now that the various $y_i$ values are stochastically distributed according to the above Gaussian distribution and \n",
"\n",
"\n",
"## Friday September 10\n",
diff --git a/doc/src/week36/week36.do.txt b/doc/src/week36/week36.do.txt
index 44994a66a..aef89b5ad 100644
--- a/doc/src/week36/week36.do.txt
+++ b/doc/src/week36/week36.do.txt
@@ -674,7 +674,16 @@ probability distribution of our targets is also a normal distribution
but now with mean value $\bm{X}\hat{\bm{\beta}}$. This means that a
single output $y_i$ is given by the Gaussian distribution
+!bt
+\[
+y_i\sim \mathcal{N}(\bm{X}{i,*}\bm{\beta}, \sigma^2)=\frac{1}{\sqrt{2\pi\sigma^2}}\exp{\left[-\frac{(y_i-\bm{X}{i,*}\bm{\beta})^2}{2\sigma^2}\right]}.
+\]
+!et
+!split
+===== Independent and Identically Distrubuted (iid) =====
+
+We assume now that the various $y_i$ values are stochastically distributed according to the above Gaussian distribution and
!split