Detecting episodes of star formation using Bayesian model selection.

Simple item page
dc.contributor.advisorTubbs, Jack Dale.
dc.creatorLawler, Andrew Joseph, 1985-
dc.creator.orcid0000-0002-2826-7431
dc.date.accessioned2020-09-04T18:18:55Z
dc.date.available2020-09-04T18:18:55Z
dc.date.created2019-12
dc.date.issued2019-10-30
dc.date.submittedDecember 2019
dc.date.updated2020-09-04T18:18:55Z
dc.description.abstractBayesian model comparison is a data-driven method to establish model complexity. In this dissertation we investigate its use in detecting multiple episodes of star formation from the analysis of the Spectral Energy Distribution (SED) of galaxies. This method is validated by simulating galaxy catalogs modeled after 3D-HST galaxies at redshift z ∼ 1. The SED of galaxies are derived using multivariate kernel density estimates of the input parameter distributions before fitting results and Bayes factors for multiple scenarios of nested models. In addition, we investigate the role that prior specification has in the derivation of physical parameters. These results are then compared to Bayes factors calculated using the Savage-Dickey Density Ratio (SDDR). The results of this investigation indicate that the use of Bayes factors are a promising tool when the model has a high level of complexity. We also demonstrate that the choice of priors plays an important role in the accuracy of results and that the SDDR is a good proxy for the Bayes factor. This last finding has significant computational advantages when compared with the computationally intensive nested sampling algorithms.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2104/10982
dc.language.isoen
dc.rights.accessrightsNo access - contact librarywebmaster@baylor.edu
dc.subjectSpectral energy distribution. Bayes factor. Savage-Dickey density ratio. Star formation.
dc.titleDetecting episodes of star formation using Bayesian model selection.
dc.typeThesis
dc.type.materialtext
local.embargo.lift2024-12-01
local.embargo.terms2024-12-01
thesis.degree.departmentBaylor University. Dept. of Statistical Science.
thesis.degree.grantorBaylor University
thesis.degree.levelDoctoral
thesis.degree.namePh.D.

Files

Original bundle

Now showing 1 - 2 of 2
No Thumbnail Available
Name:
LAWLER-DISSERTATION-2019.pdf
Size:
3.41 MB
Format:
Adobe Portable Document Format
Download
No Thumbnail Available
Name:
Andrew_Lawler_copyrightavailabilityform.pdf
Size:
99.31 KB
Format:
Adobe Portable Document Format
Download

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
LICENSE.txt
Size:
1.95 KB
Format:
Plain Text
Description:
Download

Collections

Electronic Theses and Dissertations
Theses/Dissertations - Statistical Sciences