This belief network is also known as "Asia", and is an example which is popular for introducing belief networks. It is from Lauritzen&Spiegelhalter88 (see below). It is for example purposes only, and should not be used for real decision making.
It is a simplified version of a network that could be used to diagnose patients arriving at a clinic. Each node in the network corresponds to some condition of the patient, for example, "Visit to Asia" indicates whether the patient recently visited Asia. To diagnose a patient, values are entered for nodes when they are known. Netica then automatically re-calculates the probabilities for all the other nodes, based on the relationships between them. The links between the nodes indicate how the relationships between the nodes are structured.
The two top nodes are for predispositions which influence the likelihood of the diseases. Those diseases appear in the row below them. At the bottom are symptoms of the diseases. To a large degree, the links of the network correspond to causation. This is a common structure for diagnostic networks: predisposition nodes at the top, with links to nodes representing internal conditions and failure states, which in turn have links to nodes for observables. Often there are many layers of nodes representing internal conditions, with links between them representing their complex inter-relationships.
This network is from Lauritzen, Steffen L. and David J. Spiegelhalter (1988) "Local computations with probabilities on graphical structures and their application to expert systems" in Journal Royal Statistics Society B, 50(2), 157-194.
Keep in mind when doing tutorials that there is a great deal of assitance available from Netica's onscreen help, often about the exact networks of the tutorials. For this example, choose Help->Contents/Index,
All the information contained in a belief network can be observed by examining 3 things.
- First, there is the network structure, consisting of the nodes and their links, which you can see in the network diagram currently being displayed.
- Second, are the properties of each node, which you can see in their node dialog box, obtained by double-clicking on the node.
- Third, are the actual relationships between the nodes, which you can see by
single-clicking on a node to select it, then choosing Relation(
)->View/Edit.
The relationship may be probabilistic or functional. For example, click on
"Lung Cancer", and then choose Relation->View/Edit, to see its probabilistic relation
with Smoking (the numbers are for example purposes only, and may not reflect reality).
If you click on "Tuberculosis or Cancer", and choose Relation->View/Edit, you can see
its functional dependence on Tuberculosis and Lung Cancer.
To compile the network for use, click on its window to make it active,
and choose Network->Compile (which also has a shortcut button: 
).
After compililng, the appropriate data structures for fast inference will have been built internally, and the bars in each node will have darkened, indicating that they and the numbers beside them are now valid data. The indicate the probabilities of each state of the node.
Suppose we want to "diagnose" a new patient. When she first enters the clinic, without having any information about her, we believe she has lung cancer with a probability of 5.5%, as can be seen on the Lung Cancer node (the number may be higher than that for the general population, because something has led her to the chest clinic).
If she has an abnormal x-ray, that information can be entered by clicking on the word
"Abnormal" of the "XRay Result" node:
(in a real-world belief network, you would probably be able to enter in exactly what way the x-ray was "abnormal").
All the probability numbers and bars will change to take into account the finding. Now the probability that she has lung cancer has increased to 48.9%:
If you further indicate that she has made a visit to asia recently, by clicking on "Visit", the probability of lung cancer decreases to 37.1%, because the abnormal XRay is partially explained away by a greater chance of Tuberculosis (which she could catch in Asia):
Note: old fashioned medical expert systems had problems with this kind of reasoning, since each of the findings "Abnormal XRay" and "Visit to Asia" by themselves increase or leave the same the probability of lung cancer.
You can try entering and changing some more findings. To remove a finding, simply click
on its name again. If you want to remove all the findings (a new patient has just walked
in), choose Network->Remove Findings (which also has a shortcut button: 
).
This concludes lesson 1. You may wish to review it now, and try pressing all of Netica's buttons. Netica is very robust and will not crash, no matter what you do to it. Also, if ever you wish to retract an action, simply click on the Undo button (
), or to "undo an undo", the Redo button (
).
Definitions for all of Netica's functions and buttons are given here.