Sines, steps and droplets: Semiparametric Bayesian modeling of arrival time series
Instrumentation and Methods for Astrophysics
2015-06-03 v1 Applications
Abstract
I describe ongoing work developing Bayesian methods for flexible modeling of arrival time series data without binning, aiming to improve detection and measurement of X-ray and gamma-ray pulsars, and of pulses in gamma-ray bursts. The methods use parametric and semiparametric Poisson point process models for the event rate, and by design have close connections to conventional frequentist methods currently used in time-domain astronomy.
Keywords
Cite
@article{arxiv.1201.4114,
title = {Sines, steps and droplets: Semiparametric Bayesian modeling of arrival time series},
author = {Thomas J. Loredo},
journal= {arXiv preprint arXiv:1201.4114},
year = {2015}
}
Comments
4 pages, 1 figure; to appear in the proceedings of IAU Symposium 285, "New Horizons in Time Domain Astronomy" (proceedings eds. Elizabeth Griffin, Bob Hanisch, and Rob Seaman), Cambridge University Press; see http://www.physics.ox.ac.uk/IAUS285/