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An O(log2 N) SMC2 Algorithm on Distributed Memory with an Approx. Optimal L-Kernel | IEEE Conference Publication | IEEE Xplore

An O(log2 N) SMC2 Algorithm on Distributed Memory with an Approx. Optimal L-Kernel


Abstract:

Calibrating statistical models using Bayesian inference often requires both accurate and timely estimates of parameters of interest. Particle Markov Chain Monte Carlo (p-...Show More

Abstract:

Calibrating statistical models using Bayesian inference often requires both accurate and timely estimates of parameters of interest. Particle Markov Chain Monte Carlo (p-MCMC) and Sequential Monte Carlo Squared (SMC2) are two methods that use an unbiased estimate of the log-likelihood obtained from a particle filter (PF) to evaluate the target distribution. P-MCMC constructs a single Markov chain which is sequential by nature so cannot be readily parallelized using Distributed Memory (DM) architectures. This is in contrast to SMC2 which includes processes, such as importance sampling, that are described as embarrassingly parallel. However, difficulties arise when attempting to parallelize resampling. None-the-less, the choice of backward kernel, recycling scheme and compatibility with DM architectures makes SMC2 an attractive option when compared with p-MCMC. In this paper, we present an SMC2 framework that includes the following features: an optimal (in terms of time complexity) \mathcal{O}(\log_2 N) parallelization for DM architectures, an approximately optimal (in terms of accuracy) backward kernel, and an efficient recycling scheme. On a cluster of 128 DM processors, the results on a biomedical application show that SMC2 achieves up to a 70× speed-up vs its sequential implementation. It is also more accurate and roughly 54× faster than p-MCMC. A GitHub link is given which provides access to the code.
Date of Conference: 27-29 November 2023
Date Added to IEEE Xplore: 21 December 2023
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Conference Location: Bonn, Germany

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