Distributed and recursive INLA with data-and-latent-field partitioning recovers latent-field posteriors close to full-data inference while cutting cost for large spatio-temporal models.
Parallelizing MCMC via Weierstrass Sampler
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abstract
With the rapidly growing scales of statistical problems, subset based communication-free parallel MCMC methods are a promising future for large scale Bayesian analysis. In this article, we propose a new Weierstrass sampler for parallel MCMC based on independent subsets. The new sampler approximates the full data posterior samples via combining the posterior draws from independent subset MCMC chains, and thus enjoys a higher computational efficiency. We show that the approximation error for the Weierstrass sampler is bounded by some tuning parameters and provide suggestions for choice of the values. Simulation study shows the Weierstrass sampler is very competitive compared to other methods for combining MCMC chains generated for subsets, including averaging and kernel smoothing.
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2026 1verdicts
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Distributed and recursive Bayesian inference for Big Data and complex spatio-temporal models
Distributed and recursive INLA with data-and-latent-field partitioning recovers latent-field posteriors close to full-data inference while cutting cost for large spatio-temporal models.