Nonparametric Bayesian grouping methods for spatial time-series data

• DOI: 10.48550/arxiv.1306.5202
• Publication Date: 2013-01-01
• Authors: Baskerville, Edwa...; Bedford, Trevor; Reiner, Robert C.; Pascual, Mercedes
• Keywords: Methodology (stat.ME); FOS: Computer and information sciences; 0303 health sciences; 03 medical and health sciences; FOS: Biological sciences; Quantitative Biology - Quantitative Methods; Statistics - Methodology; Quantitative Methods (q-bio.QM)

11 pages, no figures<br/>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.<br/>