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Abstract Details

Activity Number: 225
Type: Topic Contributed
Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #304795
Title: Quantifying the Sensitivity of the Bayes Factor on the Choice of Prior Distribution in High-Energy Astrophysical Analysis
Author(s): Shandong Min*+
Companies: University of California at Irvine
Address: 7204 Palo Verde Rd, Irvine, CA, 92617, United States
Keywords: Bayes Factor ; spectral analysis ; prior dependency ; model selection
Abstract:

There is an important class of model selection problems in astrophysics where the standard asymptotics of the likelihood ratio test do not apply. Uncalibrated frequency based methods nonetheless remain the standard approach among astronomers. This project will study in detail the use of the Bayes Factor for emission line detection in spectral analysis. We develop a method to quantify the typically strong dependency of the Bayes Factor on the prior distribution with the aim of identifying a tenable class of priors under location-scale families where the Bayes Factor leads to a clear choice among the possible models. We compare the results with those obtained with posterior predictive p-values and the traditional likelihood ratio test. We will also talk about the efficiency and accuracy of the available methods to calculate Bayes Factors and give suggestions in the context of spectral analysis.


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