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Activity Number: 437 - Novel Bayesian Methods and Their Impacts on Scientific Applications
Type: Invited
Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #300236
Title: Data-Driven and Science-Driven Bayesian Methods in Astronomy and Solar Physics
Author(s): David A van Dyk*
Companies: Imperial College London
Keywords: Computer Models; Uncertainty Quantification; Classification; Calibration - Instrumental; Feature Detection

In recent years, technological advances have dramatically increased the quality and quantity of data available to astronomers.  Newly launched or soon-to-be launched space-based telescopes are tailored to data-collection challenges associated with specific scientific goals. These instruments provide massive new surveys resulting in new catalogs containing terabytes of data, high resolution spectrography and imaging across the electromagnetic spectrum, and incredibly detailed movies of dynamic and explosive processes in the solar atmosphere. The spectrum of new instruments is helping scientists make impressive strides in our understanding of the physical universe, but at the same time generating massive data-analytic and data-mining challenges for scientists who study the resulting data. In this talk I will illustrate and discuss the interplay of data science, Bayesian statistics, data-driven methods, and science-driven methods in the context of several problems in astrophysics.

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

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