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Activity Number: 190 - Synergy of Bayesian, Frequentist, and Fiducial Approaches in Addressing Modern Statistical Problems in Data Science
Type: Invited
Date/Time: Tuesday, August 10, 2021 : 1:30 PM to 3:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #316756
Title: Uncertainty Quantification for Object Boundaries Extracted from Spatial Point Patterns
Author(s): Thomas C. M. Lee*
Companies: University of California at Davis
Keywords: bootstrapping; confidence region; Fourier descriptor; hypothesis testing; image segmentation
Abstract:

One common problem in astronomy is reconstructing various astronomical objects from photon counts detected by telescopes and determining if such objects change over time. This motivates the current work to recover objects hidden in noisy spatial point patterns, quantify uncertainties for these objects' boundaries and finally determine if they have changed over time. This talk reports our on-going work for solving this problem.

This is joint work with Vinay Kashyap, Jue Wang and Andreas Zezas


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

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