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Beyesian Networks |
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Bayesian networks describe the nature of the probabilistic
inter-dependence of factors (or 'nodes') in an influence
diagram. Conditional probabilities for complex belief networks
can be calculated by computer algorithms using Bayes Theorem.
The model evolves as new information is collected, so that the
model constantly reflects the current state of knowledge about
the system. Bayesian networks have been used extensively to
model real world problems. A number of commercially available
software packages exist, which enable networks to be constructed
using standard desktop computers.
(Click to enlarge image) |
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Key Ideas |
Beyesian networks are a way to model aspects of a system
when certain conditional probabilities are known or can
be estimated.
Beyesian networks may help develop values for consequence tables
once objectives and alternatives have been defined. |
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