Title: Spectral subsampling MCMC for multivariate time series
Authors: Mattias Villani - Stockholm University (Sweden) [presenting]
Matias Quiroz - University of Technology Sydney (Australia)
Robert Kohn - University of New South Wales (Australia)
Robert Salomone - University of New South Wales (Australia)
Abstract: Bayesian inference using Markov Chain Monte Carlo (MCMC) on large datasets has developed rapidly in recent years, particularly pseudo-marginal approaches based on efficient subsampling of conditionally independent observations. Spectral Subsampling MCMC extends the algorithms to univariate stationary time series with a large number of observations, for example, high-frequency data. The extension to multivariate stationary time series is presented.