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Journal of Mathematical Imaging and Vision

, Volume 61, Issue 9, pp 1258–1275 | Cite as

Rare-Event Detection by Quasi-Wang–Landau Monte Carlo Sampling with Approximate Bayesian Computation

  • Junseok KwonEmail author
Article
  • 44 Downloads

Abstract

We propose a new rare-event detection method based on quasi-Wang–Landau Monte Carlo (QWLMC) sampling with approximate Bayesian computation (ABC) called QWLMC-ABC. QWLMC-ABC integrates ABC and a Halton sequence into Wang–Landau Monte Carlo (WLMC) sampling methods. The Halton sequence provides an improved proposal function and increases the accuracy of WLMC sampling, which results in QWLMC sampling. ABC approximates a likelihood function and boosts the speed of QWLMC sampling, which yields QWLMC-ABC. QWLMC-ABC is applied to estimate the rareness of events in a statistical manner. Experimental results demonstrate that our method is comparable to state-of-the-art methods. Compared with sampling-based approaches including WLMC and QWLMC sampling, QWLMC-ABC localizes rare events at a fraction of the computation time.

Keywords

Rare-event detection Wang–Landau Monte Carlo Approximate Bayesian computation Halton sequence 

Notes

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (No. 2017R1C1B1003354).

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringChung-Ang UniversitySeoulKorea

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