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Computational Statistics

, Volume 34, Issue 1, pp 281–299 | Cite as

Neural network gradient Hamiltonian Monte Carlo

  • Lingge LiEmail author
  • Andrew Holbrook
  • Babak Shahbaba
  • Pierre Baldi
Original paper
  • 77 Downloads

Abstract

Hamiltonian Monte Carlo is a widely used algorithm for sampling from posterior distributions of complex Bayesian models. It can efficiently explore high-dimensional parameter spaces guided by simulated Hamiltonian flows. However, the algorithm requires repeated gradient calculations, and these computations become increasingly burdensome as data sets scale. We present a method to substantially reduce the computation burden by using a neural network to approximate the gradient. First, we prove that the proposed method still maintains convergence to the true distribution though the approximated gradient no longer comes from a Hamiltonian system. Second, we conduct experiments on synthetic examples and real data to validate the proposed method.

Keywords

Bayesian inference MCMC Neural networks 

Notes

Acknowledgements

Babak Shahbaba is supported by NSF Grant DMS1622490 and NIH Grant R01MH115697.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Donald Bren School of Information and Computer SciencesUniversity of CaliforniaIrvineUSA
  2. 2.Department of Human Genetics, David Geffen School of MedicineUniversity of CaliforniaLos AngelesUSA

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