Bayesian cross validation for gravitational-wave searches in pulsar-timing array data. Issue 3 (4th June 2019)
- Record Type:
- Journal Article
- Title:
- Bayesian cross validation for gravitational-wave searches in pulsar-timing array data. Issue 3 (4th June 2019)
- Main Title:
- Bayesian cross validation for gravitational-wave searches in pulsar-timing array data
- Authors:
- Wang, Haochen
Taylor, Stephen R
Vallisneri, Michele - Abstract:
- ABSTRACT: Gravitational-wave data analysis demands sophisticated statistical noise models in a bid to extract highly obscured signals from data. In Bayesian model comparison, we choose among a landscape of models by comparing their marginal likelihoods. However, this computation is numerically fraught and can be sensitive to arbitrary choices in the specification of parameter priors. In Bayesian cross validation, we characterize the fit and predictive power of a model by computing the Bayesian posterior of its parameters in a training data set, and then use that posterior to compute the averaged likelihood of a different testing data set. The resulting cross-validation scores are straightforward to compute; they are insensitive to prior tuning; and they penalize unnecessarily complex models that overfit the training data at the expense of predictive performance. In this article, we discuss cross validation in the context of pulsar-timing-array data analysis, and we exemplify its application to simulated pulsar data (where it successfully selects the correct spectral index of a stochastic gravitational-wave background), and to a pulsar data set from the NANOGrav 11-yr release (where it convincingly favours a model that represents a transient feature in the interstellar medium). We argue that cross validation offers a promising alternative to Bayesian model comparison, and we discuss its use for gravitational-wave detection, by selecting or refuting models that include aABSTRACT: Gravitational-wave data analysis demands sophisticated statistical noise models in a bid to extract highly obscured signals from data. In Bayesian model comparison, we choose among a landscape of models by comparing their marginal likelihoods. However, this computation is numerically fraught and can be sensitive to arbitrary choices in the specification of parameter priors. In Bayesian cross validation, we characterize the fit and predictive power of a model by computing the Bayesian posterior of its parameters in a training data set, and then use that posterior to compute the averaged likelihood of a different testing data set. The resulting cross-validation scores are straightforward to compute; they are insensitive to prior tuning; and they penalize unnecessarily complex models that overfit the training data at the expense of predictive performance. In this article, we discuss cross validation in the context of pulsar-timing-array data analysis, and we exemplify its application to simulated pulsar data (where it successfully selects the correct spectral index of a stochastic gravitational-wave background), and to a pulsar data set from the NANOGrav 11-yr release (where it convincingly favours a model that represents a transient feature in the interstellar medium). We argue that cross validation offers a promising alternative to Bayesian model comparison, and we discuss its use for gravitational-wave detection, by selecting or refuting models that include a gravitational-wave component. … (more)
- Is Part Of:
- Monthly notices of the Royal Astronomical Society. Volume 487:Issue 3(2019)
- Journal:
- Monthly notices of the Royal Astronomical Society
- Issue:
- Volume 487:Issue 3(2019)
- Issue Display:
- Volume 487, Issue 3 (2019)
- Year:
- 2019
- Volume:
- 487
- Issue:
- 3
- Issue Sort Value:
- 2019-0487-0003-0000
- Page Start:
- 3644
- Page End:
- 3649
- Publication Date:
- 2019-06-04
- Subjects:
- gravitational waves -- methods: statistical -- pulsars: general
Astronomy -- Periodicals
Periodicals
520.5 - Journal URLs:
- http://mnras.oxfordjournals.org/ ↗
http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1365-2966 ↗
http://www.blackwell-synergy.com/issuelist.asp?journal=mnr ↗
http://www.blackwell-synergy.com/loi/mnr ↗
http://ukcatalogue.oup.com/ ↗ - DOI:
- 10.1093/mnras/stz1537 ↗
- Languages:
- English
- ISSNs:
- 0035-8711
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5943.000000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 26997.xml