Abstract
Probabilistic graphical models, such as Bayesian networks, allow representing conditional independence information of random variables. These relations are graphically represented by the presence and absence of arcs and edges between vertices. Probabilistic graphical models are nonunique representations of the independence information of a joint probability distribution. However, the concept of Markov equivalence of probabilistic graphical models is able to offer unique representations, called essential graphs. In this survey paper the theory underlying these concepts is reviewed.
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Flesch, I., Lucas, P.J. (2007). Markov Equivalence in Bayesian Networks. In: Lucas, P., Gámez, J.A., Salmerón, A. (eds) Advances in Probabilistic Graphical Models. Studies in Fuzziness and Soft Computing, vol 213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68996-6_1
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DOI: https://doi.org/10.1007/978-3-540-68996-6_1
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