A two-stage MCMC algorithm for large ordinal spatio-temporal data fits independent per-location posterior chains in parallel, then reweights them with a cheap Metropolis-Hastings ratio to restore spatial dependence and sample the full posterior.
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Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought
A two-stage MCMC algorithm for large ordinal spatio-temporal data fits independent per-location posterior chains in parallel, then reweights them with a cheap Metropolis-Hastings ratio to restore spatial dependence and sample the full posterior.