Bayesian modeling and analysis for gradients in spatiotemporal processes.

ABSTRACT: Stochastic process models are widely employed for analyzing spatiotemporal datasets in various scientific disciplines including, but not limited to, environmental monitoring, ecological systems, forestry, hydrology, meteorology, and public health. After inferring on a spatiotemporal process for a given dataset, inferential interest may turn to estimating rates of change, or gradients, over space and time. This manuscript develops fully model-based inference on spatiotemporal gradients under continuous space, continuous time settings. Our contribution is to offer, within a flexible spatiotemporal process model setting, a framework to estimate arbitrary directional gradients over space at any given timepoint, temporal derivatives at any given spatial location and, finally, mixed spatiotempor

SUBMITTER: Quick H 

PROVIDER: S-EPMC4575262 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature