Seminar - Spatial Interpolation via Bayesian Multiscale Clustering

09/30/2014 - 11:00am
ML Chapman

Spatial Interpolation via Bayesian Multiscale Clustering

Zachary Thomas, The Ohio State University

Classical techniques for spatial interpolation have relied on the specification a stationary/isotropic covariance model for the characterization of spatial coherence. However, there is much interest in the development of more flexible methodologies capable of approximating the complex, nonstationary spatial dependence which often manifests itself in geophysical (and other) processes. Current techniques for achieving this have focused on either geometric deformations of the spatial domain or the construction of spatially varying covariance models. We present an alternative approach in which covariance is not directly modeled. Rather, dependence structure is learned via the construction of a multiresolution Bayesian spatial clustering process. Induced covariance is then naturally data-driven, nonstationary, and anisotropic. Example applications for several geostatistical problems will be presented.

Tuesday, September 30, 2014

Mesa Laboratory - Chapman Room