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Samples

Sample Bayesian Networks

These are networks used in the tutorial. Some of these are answers to exercises.

All of the networks can be found at: http://pluto.coe.fsu.edu/BNinEA/Examples/SampleBayesianNetworks/ (tarball, zip)

Dancer network

This is a potential answer to a question which asks the participants to draw a Bayesian network for several dancers each of which give several performances, each of which are judged by several raters.

Picture of dancers example.

Netica network

Note: This example is a graphical structure only, no probabilities.

Drivers licence network

An activity we often do during the tutorial is to have the participants build a network for a driver's licence examination (or a subset of that examination). This was a solution from one of the participant groups.

Picture of possible driver's licence test solution.

Netica network

Note: This example is a graphical structure only, no probabilities.

Accident Proneness

This example is taken from Feller (1968) Invalid BibTex Entry! and shows that even though accidents happen independently in each year, they are not independent because of the hidden driving proficiency variable. This illustrates the idea of conditional independence?.

Picture of Accident Prone network.

Netica network

HIV Test

This is an example of what is known as the rare disease problem. A common fallacy when evaluating evidence is to just look at the evidence and ignore the base rate. This comes up particularly in screening tests for rare diseases. The source of the numbers in this example are given in Chapter 3 of Bayesian Networks in Educational Assessment.

HIV test prior to seeing evidence.

prior

HIV test after seeing positive evidence.

positive evidence

HIV test after seeing negative evidence.

negative evidence

Netica network

Combination of two skills

This little example explores the difference between three different design patterns for combining the evidence from two skills: Compensatory?, Conjunctive? and Disjunctive?. There are three independent networks, each with the same marginal probabilities but different conditional probability tables for the observation node. Details can be found in Chapter 6 of Bayesian networks in Educational Assessment.

Picture of 3 design patterns.

Netica network

Some things to try with this network:

  1. Set the observable variables to positive and negative values, then look at what the posterior values of the skills are.
  2. Set the first skill to be known true and look at the effect on the other skill of positive and negative evidence.
  3. Set the first skill to be known false and look at the effect of evidence on the other skill.
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Page last modified on November 28, 2014, at 02:23 PM