Analysis of Energy Spectra with Low Photon Counts via Bayesian Posterior   Simulation
  Over the past 10 years Bayesian methods have rapidly grown more popular as several computationally intensive statistical algorithms have become feasible with increased computer power. In this paper, we begin with a general description of the Bayesian paradigm for statistical inference and the various state-of-the-art model fitting techniques that we employ (e.g., Gibbs sampler and Metropolis- Hastings). These algorithms are very flexible and can be used to fit models that account for the highly hierarchical structure inherent in the collection of high-quality spectra and thus can keep pace with the accelerating progress of new space telescope designs. The methods we develop, which will soon be available in the CIAO software package, explicitly model photon arrivals as a Poisson process and, thus, have no difficulty with high resolution low count X-ray and gamma-ray data. We expect these methods to be useful not only for the recently launched Chandra X-ray observatory and XMM but also new generation telescopes such as Constellation X, GLAST, etc. In the context of two examples (Quasar S5 0014+813 and Hybrid-Chromosphere Supergiant Star alpha TrA) we illustrate a new highly structured model and how Bayesian posterior sampling can be used to compute estimates, error bars, and credible intervals for the various model parameters. 
David A. van Dyk (1), Alanna Connors (2 and 3), Vinay L. Kashyap (4),   Aneta Siemiginowska (4) ((1) Department of Statistic...