Bayesian Nonparametric Shrinkage Applied to Cepheid Star Oscillations.

Published

Journal Article

Bayesian nonparametric regression with dependent wavelets has dual shrinkage properties: there is shrinkage through a dependent prior put on functional differences, and shrinkage through the setting of most of the wavelet coefficients to zero through Bayesian variable selection methods. The methodology can deal with unequally spaced data and is efficient because of the existence of fast moves in model space for the MCMC computation. The methodology is illustrated on the problem of modeling the oscillations of Cepheid variable stars; these are a class of pulsating variable stars with the useful property that their periods of variability are strongly correlated with their absolute luminosity. Once this relationship has been calibrated, knowledge of the period gives knowledge of the luminosity. This makes these stars useful as "standard candles" for estimating distances in the universe.

Full Text

Duke Authors

Cited Authors

  • Berger, J; Jefferys, W; Müller, P

Published Date

  • January 2012

Published In

Volume / Issue

  • 27 / 1

Start / End Page

  • 3 - 10

PubMed ID

  • 24368873

Pubmed Central ID

  • 24368873

Electronic International Standard Serial Number (EISSN)

  • 2168-8745

International Standard Serial Number (ISSN)

  • 0883-4237

Language

  • eng