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My dissertation refers to these belief networks.
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Chapter 1, "An intercontinental belief network."
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Chapter 4, "Overview of the RISO belief network system." A simple example
of a distributed monitoring system. combine refers to the status
variables in monitor1, monitor2, and monitor3.
The distributed belief network implemented by these four isn't a real application.
(Neither is the example from Chapter 1, but you knew that.)
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Chapter 6, "Belief network idioms for sensors and so on."
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strange-magnitude.riso,
a small belief network which helps answer, "Is the magnitude of a variable
larger or smaller than expected?"
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alt-group.riso,
a belief network which shows variables grouped in different ways.
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simple-sensor.riso,
a simple sensor model.
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predictable-sensor.riso,
a model of a variable which is measured, and also predictable by time.
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redundant-sensors.riso,
a model of three sensors which measure the same variable.
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TRH.riso, a model
of two related variables, temperature and relative humidity.
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learn-ar-w-sensor.riso,
a model of a sensor with autoregressive noise. The parameters of the sensor
model appear as belief network variables, so they can be updated.
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Chapter 7, "Application: Selecting rates to minimize energy and demand
costs."
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Chapter 8, "Additional belief network applications."
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Tdblvg.riso, a
belief network model of part of a refrigeration system.
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damper-tbn.riso,
a temporal belief network model of a mixing box damper.
The paper, ``An
algorithm for inferences in a polytree with heterogeneous conditional distributions,''
refers to this belief network.
The paper,
``RISO:
An implementation of distributed belief networks,'' refers to these
belief networks. These are the same ones which appear in Chapter 4 of the
dissertation.
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