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Variational approximation for importance sampling

Author

Listed:
  • Xiao Su

    (Amazon)

  • Yuguo Chen

    (University of Illinois at Urbana-Champaign)

Abstract

We propose an importance sampling algorithm with proposal distribution obtained from variational approximation. This method combines the strength of both importance sampling and variational method. On one hand, this method avoids the bias from variational method. On the other hand, variational approximation provides a way to design the proposal distribution for the importance sampling algorithm. Theoretical justification of the proposed method is provided. Numerical results show that using variational approximation as the proposal can improve the performance of importance sampling and sequential importance sampling.

Suggested Citation

  • Xiao Su & Yuguo Chen, 2021. "Variational approximation for importance sampling," Computational Statistics, Springer, vol. 36(3), pages 1901-1930, September.
    • Handle: RePEc:spr:compst:v:36:y:2021:i:3:d:10.1007_s00180-021-01063-w
    DOI: 10.1007/s00180-021-01063-w
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    References listed on IDEAS

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    1. David M. Blei & Alp Kucukelbir & Jon D. McAuliffe, 2017. "Variational Inference: A Review for Statisticians," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 859-877, April.
    2. Armagan, Artin & Dunson, David, 2011. "Sparse variational analysis of linear mixed models for large data sets," Statistics & Probability Letters, Elsevier, vol. 81(8), pages 1056-1062, August.
    3. repec:dau:papers:123456789/6072 is not listed on IDEAS
    4. Nicolas Depraetere & Martina Vandebroek, 2017. "A comparison of variational approximations for fast inference in mixed logit models," Computational Statistics, Springer, vol. 32(1), pages 93-125, March.
    5. Rawya Zreik & Pierre Latouche & Charles Bouveyron, 2017. "The dynamic random subgraph model for the clustering of evolving networks," Computational Statistics, Springer, vol. 32(2), pages 501-533, June.
    6. Adrian O’Hagan & Arthur White, 2019. "Improved model-based clustering performance using Bayesian initialization averaging," Computational Statistics, Springer, vol. 34(1), pages 201-231, March.
    Full references (including those not matched with items on IDEAS)

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