Speeding up sequential Markov chain Monte Carlo methods in the context of large volumes of data from distributed sensor networks

Abstract

Advances in digital sensors, digital data storage, and communications have resulted in systems being capable of accumulating large collections of data. In light of dealing with the challenges that large volumes of data present, this work proposes solutions to inference and filtering problems within the Bayesian framework. Two novel sequential Markov chain Monte Carlo (SMCMC) frameworks are proposed for nonlinear and non-Gaussian state space models, able to deal with large volumes of data (or observations). These are SMCMC frameworks relying on two key ideas: (1) a divide-and-conquer type approach computing local filtering distributions, each using a subset of the data, and (2) subsampling the large data and utilizing a smaller subset for filtering and inference. Simulation results highlight the large computational savings that can reach 90% by the proposed algorithms when compared with a state-of-the-art SMCMC approach.