The No-U-Turn Sampler as a Proposal Distribution in a Sequential Monte Carlo Sampler without Accept/Reject

Devlin, Lee, Carter, Matthew, Horridge, Paul, Green, Peter L and Maskell, Simon
(2024) The No-U-Turn Sampler as a Proposal Distribution in a Sequential Monte Carlo Sampler without Accept/Reject. IEEE Signal Processing Letters, 31 (99). pp. 1-5.

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Markov Chain Monte Carlo (MCMC) is a method for drawing samples from non-standard probability distributions. Hamiltonian Monte Carlo (HMC) is a popular variant of MCMC that uses gradient information to explore the target distribution. The Sequential Monte Carlo (SMC) sampler is an alternative sampling method which, unlike MCMC, can readily utilise parallel computing architectures. It is typical within SMC literature to target a tempered distribution using a proposal with an accept/reject mechanism. In this letter, we show how the proposal used in the No-U-Turn Sampler (NUTS), an advanced variant of HMC, can be incorporated into an SMC sampler without an accept/reject mechanism. Empirical results show that this can remove the need for tempering and gives rise to accurate estimates being generated in fewer iterations which motivates this technique being deployed on parallel hardware.

Item Type: Article
Uncontrolled Keywords: 46 Information and Computing Sciences, 4601 Applied Computing
Divisions: Faculty of Science and Engineering > School of Engineering
Depositing User: Symplectic Admin
Date Deposited: 11 Apr 2024 07:40
Last Modified: 14 Jun 2024 16:29
DOI: 10.1109/lsp.2024.3386494
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