The Application of Markov Random Fields in Modeling Spatial Dependence and Environmental Data Correlation

Posted: Jun 28, 2013

• Jacob Scott
• Grace Walker
• Henry Smith

Abstract

Environmental data analysis presents unique challenges due to the inherent spatial dependence and complex correlation structures that characterize natural systems. Traditional spatial statistical methods, while valuable, often rely on assumptions of stationarity and Gaussianity that may not adequately capture the intricate dependencies present in environmental phenomena. Markov Random Fields (MRFs) offer a powerful alternative framework for modeling spatial dependence through local conditional distributions, providing flexibility in capturing complex dependency structures without requiring global parametric assumptions. This research develops and validates a novel methodological framework that extends MRF theory to address the specific challenges of environmental data analysis.