Abstract
Recursive Probability Trees offer a flexible framework for representing the probabilistic information in Probabilistic Graphical Models. This structure is able to provide a detailed representation of the distribution it encodes, by specifying most of the types of independencies that can be found in a probability distribution. Learning this structure involves the search for context-specific independencies along with factorisations within the available data. In this paper we develop the first approach at learning Recursive Probability Trees from data by extending an existent greedy methodology for retrieving small Recursive Probability Trees from probabilistic potentials. We test the performance of the algorithm by learning from different databases, both real and handcrafted, and we compare the performance for different databases sizes.
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References
Boutilier, C., Friedman, N., Goldszmidt, M., Koller, D.: Context-specific independence in Bayesian networks. In: Horvitz, E., Jensen, F.V. (eds.) Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence, pp. 115–123. Morgan Kaufmann (1996)
Cano, A., Gómez-Olmedo, M., Moral, S., Pérez-Ariza, C., Salmerón, A.: Learning recursive probability trees from probabilistic potentials. International Journal of Approximate Reasoning 53, 1367–13875 (2012)
Cano, A., Gómez-Olmedo, M., Moral, S., Pérez-Ariza, C., Salmerón, A.: Inference in Bayesian networks with recursive probability trees: data structure definition and operations. International Journal of Intelligent Systems 28 (2013)
Heckerman, D., Geiger, D., Chickering, D.: Learning bayesian networks: The combination of knowledge and statistical data. Machine Learning 20, 197–243 (1995)
Quinlan, J.: Induction of decision trees. Machine Learning 1, 81–106 (1986)
Salmerón, A., Cano, A., Moral, S.: Importance sampling in Bayesian networks using probability trees. Computational Statistics and Data Analysis 34, 387–413 (2000)
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Cano, A., Gómez-Olmedo, M., Moral, S., Pérez-Ariza, C.B., Salmerón, A. (2013). Learning Recursive Probability Trees from Data. In: Bielza, C., et al. Advances in Artificial Intelligence. CAEPIA 2013. Lecture Notes in Computer Science(), vol 8109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40643-0_34
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DOI: https://doi.org/10.1007/978-3-642-40643-0_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40642-3
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