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View Submission - SDS2022
Title: Data Compression and Distributed Inference in Spatial Analysis Authors:  Rajarshi Guhaniyogi - Texas A & M university (United States) [presenting]
Abstract: Bayesian data sketching for spatial regression models is introduced to obviate computational challenges presented by large numbers of spatial locations. To address the challenges of analyzing very large spatial data, we compress spatially oriented data by a random linear transformation to achieve dimension reduction and conduct inference on the compressed data. Our approach distinguishes itself from several existing methods for analyzing large spatial data in that it requires neither the development of new models or algorithms nor any specialized computational hardware while delivering fully model-based Bayesian inference. Well-established methods and algorithms for spatial regression models can be applied to compressed data. We further extend data sketching idea to offer important advantages in the realm of distributed Bayesian inference.