Spectral Subsampling MCMC for Stationary Time Series
Methodology
2020-02-18 v2 Computation
Abstract
Bayesian inference using Markov Chain Monte Carlo (MCMC) on large datasets has developed rapidly in recent years. However, the underlying methods are generally limited to relatively simple settings where the data have specific forms of independence. We propose a novel technique for speeding up MCMC for time series data by efficient data subsampling in the frequency domain. For several challenging time series models, we demonstrate a speedup of up to two orders of magnitude while incurring negligible bias compared to MCMC on the full dataset. We also propose alternative control variates for variance reduction based on data grouping and coreset constructions.
Cite
@article{arxiv.1910.13627,
title = {Spectral Subsampling MCMC for Stationary Time Series},
author = {Robert Salomone and Matias Quiroz and Robert Kohn and Mattias Villani and Minh-Ngoc Tran},
journal= {arXiv preprint arXiv:1910.13627},
year = {2020}
}
Comments
Empirical section significantly revised and extended