added Bayes stuff

doc/src/week36/week36.do.txt

@@ -1270,9 +1270,9 @@ Let us try to illustrate Bayes' theorem through an example.
 !split
 =====  Example of Usage of Bayes' theorem =====
 
-Let us suppose that you are undergoing a series of mammography scan in
+Let us suppose that you are undergoing a series of mammography scans in
 order to rule out possible breast cancer cases.  We define the
-sensitivity for a positive event by the variable $X$ (it takes binary
+sensitivity for a positive event by the variable $X$. It takes binary
 values with $X=1$ representing a positive event and $X=0$ being a
 negative event. We reserve $Y$ as a classification parameter for
 either a negative or a postive breast cancer confirmation.
@@ -1287,7 +1287,7 @@ p(X=1\vert Y=1) =0.8.
 \]
 !et
 
-This obviously sounds  scaring since many would conclude that if the test is positive, there is a likelihood of $80\%$ for having cancer.
+This obviously sounds  scary since many would conclude that if the test is positive, there is a likelihood of $80\%$ for having cancer.
 It is however not correct, as the following Bayesian analysis shows.
 
 !split
@@ -1318,6 +1318,19 @@ p(Y=1\vert X=1)=\frac{p(X=1\vert Y=1)p(Y=1)}{p(X=1\vert Y=1)p(Y=1)+p(X=1\vert Y=
 !et
 That is, in case of a positive test, there is only a $3\%$ chance of having cancer!
 
+
+!split
+===== Bayes' Theorem and Ridge and Lasso Regression =====
+
+Hitherto we have discussed Ridge and Lasso regression in terms of a
+linear analysis. This may to many of you feel rather technical and
+perhaps not that intuitive. The question is whether we can develop a
+more intuitive way of understanding what Ridge and Lasso express.
+
+Before we proceed let us perform a Ridge and OLS analysis of a polynomial fit. 
+
+
+
 !split
 ===== Why resampling methods =====