# The ABNMS BN Repository

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60 BNs found.

###### Arhopalus Flight Activity

This Bayesian network was developed to model the flight activity of Arhopalus ferus, a wood borer. The model is used to predict flight activity as a function of meteorological conditions. This contributes to the quantification of potential phytosanitary risks as it is a measure of potential exposure of export logs to flying/dispersing insects.

The data set for this model can be found at <abnms.org...>.

###### Hylastes Flight Activity

This Bayesian network was developed to model the flight activity of Hylastes ater, a bark beetle. The model is used to predict flight activity as a function of meteorological conditions. This contributes to the quantification of potential phytosanitary risks as it is a measure of potential exposure of export logs to flying/dispersing insects.

The data set for this model can be found at <abnms.org...>.

###### Hylurgus Flight Activity

This Bayesian network was developed to model the flight activity of Hylurgus ligniperda as a function of meteorological conditions. H. ligniperda is a common forest insect within New Zealand Pinus radiata plantations forests. Predicting flight activity is one step towards assessing potential phytosanitary risks of forest exports as it is an indication of the exposure of logs to flying/dispersing insects.

The data set for this model can be found at <abnms.org...>.

###### iris

Simple classification network based on the classic Iris flower data set introduced by the statistician and biologist Ronald Fisher. The data set consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimetres.

Refer: <en.wikipedia.org...>

###### Wet Grass

Many belief network researchers have used networks similar to this (possibly not containing nodes 'Neighbor's Grass' or 'Wall') to demonstrate "explaining away", and how rule based or evidence support systems may do incorrect chaining with these types of situations. For instance, they may combine the rules "If the ground is wet then it is likely it rained" with "If the sprinkler was on then it is likely the ground is wet" to get "If the sprinkler is on then it is likely it rained" when in fact the opposite is true. For example see Judea Pearl (1988) Probabilistic Reasoning in Intelligent Systems, p. 7. The probabilities for this network were chosen by Norsys.

Paper link: <www.norsys.com...>

###### Thermostat A

A time delay decision network for the thermostat-heater control problem. This is a simple example of heater control, with a single heater, single thermal mass, single sensor, and costs for overheating, underheating, energy and switching the heater on and off. It could easily be expanded into a more complex example.

###### SpiegelhalterDLC93

A 20 node example of a belief net for medical diagnosis. This file does not contain the numerical probabilities (except those few given in the paper). This, together with the paper, provide a good worked out example for clique tree (i.e. join tree) compiling and propagation.

Paper link: <www.norsys.com...>

###### Separable 2

A simple example of a separable (termed 'abnormal' by Zhang) decision net, and the 2 nets it can be separated into. See Separable1 for an even simpler example. This network shows only dependencies, and does not include any numerical relationships.

BN link: <www.norsys.com...>

###### Separable 1

The simplest example of a separable (termed 'abnormal' by Zhang) decision net, and the 2 nets it can be separated into. This network shows only dependencies, and does not include any numerical relationships.

BN link: <www.norsys.com...>

###### Reactor

This example shows that introducing a common cause can make an important change, even if the maringal probabilities of the children remain the same.

Catastrophic failure occurs only if all 3 subsystems fail.

The probability of Catastrophic_Failure1 (with no findings) is 2.9791e-011, which may be an acceptable risk.

However, more careful modeling leads to the addition of the Incorrect_Test_Procedure node. With only this change, the failure probability jumps to 0.003%, which is unacceptable.

Notice that the failure probability of the subsystems remains 0.031% in both cases, and the CPT of Catastrophic_Failure remains unchanged.

BN link: <www.norsys.com...>

###### Pathfinder

An expert system that assists surgical pathologists with the diagnosis of lymph-node diseases.

Paper link: <www.norsys.com...>

###### Oil Wildcatter Simplified

An influence diagram with decisions of whether to do seismic tests for oil, and whether to drill for oil, in order to maximize profits. Same as Oil_Wildcatter, but with some nodes absorbed ('summed out'). In wide usage, but originally from Raiffa68.

Paper link: <www.norsys.com...>

###### Neapolitan90

From the book Neapolitan, Richard E. (1990) Probabilistic Reasoning in Expert Systems: Theory and Algorithms, John Wiley & Sons, New York, p. 259. Started as problem 5.5.2, p.183, it becomes example 7.5, p. 261 (with diagram on p. 259), and continues numerically on p. 279. Originally based on the Lauritzen & Spiegelhalter 1988 paper.

BN link: <www.norsys.com...>

###### Mendel Genetics

This Bayes net is for the famous experiments of Mendel, in which he developed the foundations of hereditary genetics. The experiments involved breeding red and white flowered pea plants.

A tutorial, with a complete description, is available at <www.norsys.com...>

Paper link: <www.norsys.com...>

###### Learn Latent

This Bayes net demonstrates learning a latent (or "hidden") variable, which occurs when you have an important node in the net for which no data appears in the case file.

Paper link: <www.norsys.com...>

###### Imposs Demo

Demo of the impossible condition check. Refer to the following link for a description and tutorial of the BN: <www.norsys.com...>

Paper link: <www.norsys.com...>

###### False Barrier

In order to express the belief in a node as a function, it must be expressed as a function of the joint beliefs of its Markov boundary nodes (i.e. the beliefs in the Cartesian product of their values). Thinking in terms of paths can obscure this. Consider the False Barrier BN illustrated:

When there is no evidence, the beliefs of A, B, C, and D are all 1/2. If we get evidence TRUE for A, the beliefs of B and C remain at 1/2, but the belief at D changes to 3/4. Thinking in terms of a constraint network, or "flow of influence along paths," it is hard to see how a change at A can create a change at D without changing the beliefs at B or C. Of course, it is the joint belief in B and C which have changed (BEL (+b+c) changes from 1/4 to 3/8, BEL(+b-c) changes from 1/4 to 1/8, etc). Therefore, we must be careful with the path concept.

Paper link: <www.norsys.com...>

###### Europe Map CSP

This Bayes net demonstrates how to solve constraint satisfaction problems (CSPs) using Bayes nets. A CSP consists of a number of variables, and some constraints between them. The goal is to find values for the variables that satisfy all the constraints.

Paper link: <www.norsys.com...>

###### Classifier Optimization

A BN classifier.

An explanation of this BN can be found at <www.norsys.com...>

Paper link: <www.norsys.com...>

###### Central Limit Theorem

This Bayes net is to demonstrate the Central Limit Theorem.

Paper link: <www.norsys.com...>