Spectral Subsampling MCMC for Stationary Time Series

Understand

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.