adding more stuff

doc/src/Bayesian/Bayesian.do.txt

@@ -14,13 +14,21 @@ as some central distribution functions such as the Normal
 distribution, the binomial distribution and other probability
 distribution functions.  
 
-In essentially all the Machine Learning
-algorithms we have studied, our focus has been on a so-called
-_frequentist approach_, where knowledge of an underlying likelihood
-function has not been emphasized. Our data, whether we had a classification or a regression problem, have been our central points of departure. 
+In essentially all the Machine Learning algorithms we have studied,
+our focus has been on a so-called _frequentist approach_, where
+knowledge of an underlying likelihood function has not been
+emphasized. Our data, whether we had a classification or a regression
+problem, have been our central points of departure.
 
-Here we wish to merge this approach with the derivation of a likelihood function which can be used to make prediction on how our system under study evolves. 
-We will venture into the realm of what is called Bayesian Neural Networks. To get an overarching view on what this entails, the following figure conveys the essential differences between a standard Neural network that we have met earlier and a Bayesian Neural Network. In order to get there, we need to present some of the basic elements of Bayesian statistics, starting with the product rule and Bayes' theorem.
+Here we wish to merge this approach with the derivation of a
+likelihood function which can be used to make prediction on how our
+system under study evolves.  We will venture into the realm of what is
+called Bayesian Neural Networks. To get an overarching view on what
+this entails, the following figure conveys the essential differences
+between a standard Neural network that we have met earlier and a
+Bayesian Neural Network. In order to get there, we need to present
+some of the basic elements of Bayesian statistics, starting with the
+product rule and Bayes' theorem.
 
 !eblock