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Declarative Data Generation with ProbLog
2015
Proceedings of the Sixth International Symposium on Information and Communication Technology - SoICT 2015
In this paper we describe a novel declarative approach to data generation based on probabilistic logic programming. We show that many data generation tasks can be described as a probabilistic logic program. To this end, we extend the ProbLog language with continuous distributions and we develop a simple sampling algorithm for this language. We demonstrate that many data generation tasks can be described as a model in this language and we provide examples of generators for attribute-value data,
doi:10.1145/2833258.2833267
dblp:conf/soict/Dries15
fatcat:2nbf6cyx4rf4pidnyqwsbgkj3m