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Book Chapter

Probabilistic Modeling in Machine Learning

  • Bacciu D
  • Lisboa P
  • Sperduti A
  • et al.
Springer Berlin Heidelberg, (2015), 545-575
DOI: 10.1007/978-3-662-43505-2_31
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Abstract

S2 TL;DR: This chapter discusses how such probabilistic interactions can be mapped to directed and undirected graph structures, which are the Bayesian and Markov networks, and introduces two topical methodologies that are central to Probabilistic modeling in machine learning.

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Bacciu, D., Lisboa, P. J. G., Sperduti, A., & Villmann, T. (2015). Probabilistic Modeling in Machine Learning. In Springer Handbook of Computational Intelligence (pp. 545–575). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-43505-2_31

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