Recently, there has been an increasing interest in directed probabilistic logical models and a variety of languages for describing such models has been proposed. Although many authors provide high-level arguments to show that in principle models in their language can be learned from data, most of the proposed learning algorithms have not yet been studied in detail. We introduce an algorithm, generalized ordering-search, to learn both structure and conditional probability distributions (CPDs) of directed probabilistic logical models. The algorithm upgrades the ordering-search algorithm for Bayesian networks. We use relational probability trees as a representation for the CPDs. We present experiments on blocks world domains, a gene domain and the Cora dataset. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Ramon, J., Croonenborghs, T., Fierens, D., Blocked, H., & Bruynooghe, M. (2007). Generalized ordering-search for learning directed probabilistic logical models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4455 LNAI, pp. 40–42). Springer Verlag. https://doi.org/10.1007/978-3-540-73847-3_10
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