The included example model pavement_model
is a Bayesian network created with the bnlearn package. It's a directed acyclic graph (DAG) which describes the probabilistic relationships between Rain (Yes/No), Sprinkler (On/Off), and Pavement (Wet/Dry).
The joint probability function is:
$$ \text{Pr}(\mathbf{P}, \mathbf{S}, \mathbf{R}) = \text{Pr}(\mathbf{P}|\mathbf{S},\mathbf{R}) \text{Pr}(\mathbf{S}|\mathbf{R}) \text{Pr}(\mathbf{R}) $$
This Bayesian network can be used to answer probabilistic queries about the variables even when only a subset of variables are known. The observed nodes are evidence and approximate inference algorithms -- together with Bayes' rule -- propagate this evidence throughout the network to update the probabilities in the unobserved nodes.
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