Abstract
This chapter introduces required theoretical concepts for the definition of Bayes and Markov networks. After important elements of probability theory—especially (conditional) independences—are discussed, we present relevant graph-theoretic notions with emphasis on so-called separation criteria. These criteria will later allow us to capture probabilistic independences with an undirected or directed graph.
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© 2016 Springer-Verlag London
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Kruse, R., Borgelt, C., Braune, C., Mostaghim, S., Steinbrecher, M. (2016). Elements of Probability and Graph Theory. In: Computational Intelligence. Texts in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-7296-3_22
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DOI: https://doi.org/10.1007/978-1-4471-7296-3_22
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