ProbLog2: Probabilistic Logic ProgrammingShow others and affiliations
2015 (English)In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part III / [ed] Albert Bifet, Michael May, Bianca Zadrozny, Ricard Gavalda, Dino Pedreschi, Francesco Bonchi, Jaime Cardoso, Myra Spiliopoulou, Springer, 2015, Vol. 9286, p. 312-315Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]
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.
Place, publisher, year, edition, pages
Springer, 2015. Vol. 9286, p. 312-315
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 9286
Keywords [en]
Probabilistic programming, Probabilistic inference, Parameter learning
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:oru:diva-91690DOI: 10.1007/978-3-319-23461-8_37ISI: 000363667400040Scopus ID: 2-s2.0-84984640486ISBN: 978-3-319-23460-1 (print)ISBN: 978-3-319-23461-8 (electronic)OAI: oai:DiVA.org:oru-91690DiVA, id: diva2:1553022
Conference
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2015), Porto, Portugal, September 7-11, 2015
2021-05-072021-05-072021-05-07Bibliographically approved