Abstract
Probabilistic networks are graphical models of (causal) interactions among a set of variables, where the variables are represented as vertices (nodes) of a graph and the interactions (direct dependences) as directed edges (links or arcs) between the vertices. Any pair of unconnected vertices of such a graph indicates (conditional) independence between the variables represented by these vertices under particular circumstances that can easily be read from the graph. Hence, probabilistic networks capture a set of (conditional) dependence and independence properties associated with the variables represented in the network.
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Notes
- 1.
\(\mathcal{L}(X)\) should be read as “the law of X.”
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© 2013 Springer Science+Business Media New York
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Kjærulff, U.B., Madsen, A.L. (2013). Probabilistic Networks. In: Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis. Information Science and Statistics, vol 22. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5104-4_4
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DOI: https://doi.org/10.1007/978-1-4614-5104-4_4
Publisher Name: Springer, New York, NY
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