Bayesian networks. Bayesian Network Motivation. We want a representation and reasoning system that is based on conditional independence Compact yet expressive representation Efficient reasoning procedures Bayesian Networks are such a representation
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The graph-structured approximation
The full joint distribution
network topology reflects causal knowledge
Shouldn’t these add up to 1?
No. Each row adds up to 1, and we’re using this to let us show only half of the table. For example,
What is P(j m a b e)?
P (j | a) P (m | a) P (a | b, e) P (b) P (e)
You want to diagnose whether there is a fire in a building
First you choose the variables. In this case, all are Boolean: