Graphical model inference : Sequential Monte Carlo meets deterministic approximations
Lindsten, F., Helske, J., & Vihola, M. (2018). Graphical model inference : Sequential Monte Carlo meets deterministic approximations. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, & R. Garnett (Eds.), NeurIPS 2018 : Proceedings of the 32nd Conference on Neural Information Processing Systems. Neural Information Processing Systems Foundation, Inc.. Advances in Neural Information Processing Systems, 31. https://papers.nips.cc/paper/8041-graphical-model-inference-sequential-monte-carlo-meets-deterministic-approximations
Published inAdvances in Neural Information Processing Systems
© The Authors & NIPS, 2018.
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.
PublisherNeural Information Processing Systems Foundation, Inc.
ConferenceAdvances in neural information processing systems
Is part of publicationNeurIPS 2018 : Proceedings of the 32nd Conference on Neural Information Processing Systems
Publication in research information system
MetadataShow full item record
Related funder(s)Academy of Finland
Funding program(s)Research costs of Academy Research Fellow, AoF; Research post as Academy Research Fellow, AoF
Additional information about fundingFL has received support from the Swedish Foundation for Strategic Research (SSF) via the project Probabilistic Modeling and Inference for Machine Learning (contract number: ICA16-0015) and from the Swedish Research Council (VR) via the projects Learning of Large-Scale Probabilistic Dynamical Models (contract number: 2016-04278) and NewLEADS – New Directions in Learning Dynamical Systems (contract number: 621-2016-06079). JH and MV have received support from the Academy of Finland (grants 274740, 284513 and 312605). ...
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