Nonparametric Bayesian grouping methods for spatial time-series data
Quantitative Methods
2013-06-24 v1 Methodology
Abstract
We describe an approach for identifying groups of dynamically similar locations in spatial time-series data based on a simple Markov transition model. We give maximum-likelihood, empirical Bayes, and fully Bayesian formulations of the model, and describe exhaustive, greedy, and MCMC-based inference methods. The approach has been employed successfully in several studies to reveal meaningful relationships between environmental patterns and disease dynamics.
Keywords
Cite
@article{arxiv.1306.5202,
title = {Nonparametric Bayesian grouping methods for spatial time-series data},
author = {Edward B. Baskerville and Trevor Bedford and Robert C. Reiner and Mercedes Pascual},
journal= {arXiv preprint arXiv:1306.5202},
year = {2013}
}
Comments
11 pages, no figures