Neural network gradient Hamiltonian Monte Carlo

Lingge Li (), Andrew Holbrook (), Babak Shahbaba and Pierre Baldi
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Lingge Li: University of California
Andrew Holbrook: University of California
Babak Shahbaba: University of California
Pierre Baldi: University of California

Computational Statistics, 2019, vol. 34, issue 1, No 12, 299 pages

Abstract: 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 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00180-018-00861-z

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