Journal
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, PT III
Volume 9286, Issue -, Pages 312-315Publisher
SPRINGER-VERLAG BERLIN
DOI: 10.1007/978-3-319-23461-8_37
Keywords
Probabilistic programming; Probabilistic inference; Parameter learning
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We present ProbLog2, the state of the art implementation of the probabilistic programming language ProbLog. The ProbLog language allows the user to intuitively build programs that do not only encode complex interactions between a large sets of heterogenous components but also the inherent uncertainties that are present in real-life situations. The system provides efficient algorithms for querying such models as well as for learning their parameters from data. It is available as an online tool on the web and for download. The offline version offers both command line access to inference and learning and a Python library for building statistical relational learning applications from the system's components.
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