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Activity Number: 56 - Modern Bayesian Methods for Complex Spatial Data
Type: Topic Contributed
Date/Time: Sunday, August 7, 2022 : 4:00 PM to 5:50 PM
Sponsor: International Society for Bayesian Analysis (ISBA)
Abstract #322192
Title: Curvature Processes: Directional Concavity in Gaussian Random Fields
Author(s): Aritra Halder*
Companies: University of Virginia
Keywords: Bayesian modeling; Directional Curvature; Gaussian Processes; Wombling

Spatial process models offer a rich framework for modeling dependence in response variables arising from diverse scientific domains. Analyzing the resulting random surface provides deeper insights into the nature of latent dependence within the studied response. We develop Bayesian modeling and inference for rapid changes on the response surface to assess directional curvature along a given trajectory. Such trajectories or curves of rapid change, often referred to as \emph{wombling} boundaries, occur in geographic space in the form of rivers in a flood plain, roads, mountains or plateaus or other topographic features lead to high gradients on the response surface. We demonstrate fully model based Bayesian inference on directional curvature processes to analyze differential behavior in responses along wombling boundaries. We illustrate our methodology with a number of simulated experiments followed by multiple applications featuring the Boston Housing data; Meuse river data; and temperature data from the Northeastern United States.

Authors who are presenting talks have a * after their name.

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