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Automated learning with a probabilistic programming language: Birch
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. (Automatic Control)
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.ORCID iD: 0000-0001-5183-234X
2018 (English)In: Annual Reviews in Control, ISSN 1367-5788, E-ISSN 1872-9088, Vol. 46, p. 29-43Article in journal (Refereed) Published
Abstract [en]

This work offers a broad perspective on probabilistic modeling and inference in light of recent advances in probabilistic programming, in which models are formally expressed in Turing-complete programming languages. We consider a typical workflow and how probabilistic programming languages can help to automate this workflow, especially in the matching of models with inference methods. We focus on two properties of a model that are critical in this matching: its structure—the conditional dependencies between random variables—and its form—the precise mathematical definition of those dependencies. While the structure and form of a probabilistic model are often fixed a priori, it is a curiosity of probabilistic programming that they need not be, and may instead vary according to random choices made during program execution. We introduce a formal description of models expressed as programs, and discuss some of the ways in which probabilistic programming languages can reveal the structure and form of these, in order to tailor inference methods. We demonstrate the ideas with a new probabilistic programming language called Birch, with a multiple object tracking example.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 46, p. 29-43
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:uu:diva-368615DOI: 10.1016/j.arcontrol.2018.10.013ISI: 000453618200003OAI: oai:DiVA.org:uu-368615DiVA, id: diva2:1268437
Funder
Swedish Foundation for Strategic Research , RIT15-0012Available from: 2018-12-05 Created: 2018-12-05 Last updated: 2019-01-16Bibliographically approved

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Schön, Thomas B.

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