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
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Advances in Neural Information Processing SystemsDate
2018Copyright
© 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.
Publisher
Neural Information Processing Systems Foundation, Inc.Conference
Advances in neural information processing systemsIs part of publication
NeurIPS 2018 : Proceedings of the 32nd Conference on Neural Information Processing SystemsISSN Search the Publication Forum
1049-5258
Original source
https://papers.nips.cc/paper/8041-graphical-model-inference-sequential-monte-carlo-meets-deterministic-approximationsPublication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/28980157
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Academy of FinlandFunding program(s)
Academy Research Fellow, AoF; Research costs of Academy Research Fellow, AoFAdditional information about funding
FL 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). ...License
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