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- W2273875315 abstract "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, sequences, graphs and logical interpretations and we show how to model common extensions such as noise, missing values and concept drift." @default.
- W2273875315 created "2016-06-24" @default.
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- W2273875315 date "2015-12-03" @default.
- W2273875315 modified "2023-09-24" @default.
- W2273875315 title "Declarative Data Generation with ProbLog" @default.
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- W2273875315 doi "https://doi.org/10.1145/2833258.2833267" @default.
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