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Graphical model inference: Sequential Monte Carlo meets deterministic approximations
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
Linkoping Univ, Dept Sci & Technol, Norrkoping, Sweden.
Univ Jyvaskyla, Dept Math & Stat, Jyvaskyla, Finland.
2018 (English)In: ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018) / [ed] Bengio, S Wallach, H Larochelle, H Grauman, K CesaBianchi, N Garnett, R, NEURAL INFORMATION PROCESSING SYSTEMS (NIPS) , 2018, Vol. 31Conference paper, Published paper (Refereed)
Abstract [en]

Approximate inference in probabilistic graphical models (PGMs) can be grouped into deterministic methods and Monte-Carlo-based methods. The former can often provide accurate and rapid inferences, but are typically associated with biases that are hard to quantify. The latter enjoy asymptotic consistency, but can suffer from high computational costs. In this paper we present a way of bridging the gap between deterministic and stochastic inference. Specifically, we suggest an efficient sequential Monte Carlo (SMC) algorithm for PGMs which can leverage the output from deterministic inference methods. While generally applicable, we show explicitly how this can be done with loopy belief propagation, expectation propagation, and Laplace approximations. The resulting algorithm can be viewed as a post-correction of the biases associated with these methods and, indeed, numerical results show clear improvements over the baseline deterministic methods as well as over "plain" SMC.

Place, publisher, year, edition, pages
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS) , 2018. Vol. 31
Series
Advances in Neural Information Processing Systems, ISSN 1049-5258 ; 31
National Category
Probability Theory and Statistics Computational Mathematics
Identifiers
URN: urn:nbn:se:uu:diva-382031ISI: 000461852002071OAI: oai:DiVA.org:uu-382031DiVA, id: diva2:1306615
Conference
32nd Conference on Neural Information Processing Systems (NIPS), DEC 02-08, 2018, Montreal, CANADA
Funder
Swedish Foundation for Strategic Research , ICA16-0015Swedish Research Council, 2016-04278Available from: 2019-04-24 Created: 2019-04-24 Last updated: 2019-04-24Bibliographically approved

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https://papers.nips.cc/book/advances-in-neural-information-processing-systems-31-2018

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Lindsten, Fredrik

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