From 1b6ff62c924540b043bac8c48a0937c38e7d5155 Mon Sep 17 00:00:00 2001 From: mhjensen Date: Wed, 10 Jul 2019 13:47:13 +0200 Subject: [PATCH] adding more stuff --- doc/src/Bayesian/Bayesian.do.txt | 20 ++++++++++++++------ 1 file changed, 14 insertions(+), 6 deletions(-) diff --git a/doc/src/Bayesian/Bayesian.do.txt b/doc/src/Bayesian/Bayesian.do.txt index 90f65f225..e03dbf10d 100644 --- a/doc/src/Bayesian/Bayesian.do.txt +++ b/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