This paper is from Session 4I: Integrating Bayesian methods with traditional statistics education

which comes under Topic 4: Statistics education at the post secondary (tertiary) level

Other papers under session 4I ... 4I1: Psychology students’ understanding of elementary Bayesian inference 4I2: Comparing the Bayesian and likelihood approaches to inference: a... 4I3: The very beginning of a class on inference: classical vs Bayesian

(Tuesday 13th, 14:00-16:00)

Teaching young grownups how to use Bayesian networks

Presenter

• Stefan Krauss (University of Regensburg, Germany)

Co-authors

• Georg Bruckmaier (University of Regensburg, Germany)
• Laura Martignon (Ludwigsburg University of Education, Germany)

Abstract

A Bayesian network, or directed acyclic graphical model is a probabilistic graphical model that represents conditional dependencies and conditional independencies of a set of random variables. Each node is associated with a probability function that takes as input a particular set of values for the node’s parent variables and gives the probability of the variable represented by the node, conditioned on the values of its parent nodes. Links represent probabilistic dependencies, while the absence of a link between two nodes denotes a conditional independence between them. Bayesian networks can be updated by means of Bayes’ Theorem. Because Bayesian networks are a powerful representational and computational tool for probabilistic inference, it makes sense to instruct young grownups on their use and even provide familiarity with software packages like Netica. We present introductory schemes with a variety of examples.

Other papers under session 4I ... 4I1: Psychology students’ understanding of elementary Bayesian inference 4I2: Comparing the Bayesian and likelihood approaches to inference: a... 4I3: The very beginning of a class on inference: classical vs Bayesian