Multi-Stage MCMC for Spatio-Temporal Data with an Application to the U.S. Drought Monitor

Music City Center 

Bayesian analyses of large spatio-temporal data are hindered by the computational expense required to implement Markov chain Monte Carlo (MCMC) algorithms. A common solution is for researchers to reduce the dimensionality by making simplifying assumptions in the spatial and/or temporal structure. While this increases computational efficiency, it comes at the cost of model flexibility. Alternatively, we propose a multi-stage MCMC approach that
permits Bayesian analysis of the full model. The use of parallel computing alleviates the computational expense associated with large space-time data, making this approach scalable and generalizable to a flexible class of spatio-
temporal models. This work is motivated by spatio-temporal ordinal data from the US Drought Monitor. This weekly data product records drought conditions across the United States as one of six ordered levels. We develop a Bayesian
spatio-temporal ordinal model for modeling and forecasting drought conditions, and we fit this model with the proposed multi-stage MCMC approach.

Bayesian computing

MCMC

parallel computing

spatial

environmental