Journal Article

Computational statistics using the Bayesian Inference Engine

Martin D. Weinberg

in Monthly Notices of the Royal Astronomical Society

Published on behalf of The Royal Astronomical Society

Volume 434, issue 2, pages 1736-1755
Published in print September 2013 | ISSN: 0035-8711
Published online July 2013 | e-ISSN: 1365-2966 | DOI: http://dx.doi.org/10.1093/mnras/stt1132
Computational statistics using the Bayesian Inference Engine

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This paper introduces the Bayesian Inference Engine (bie), a general parallel, optimized software package for parameter inference and model selection. This package is motivated by the analysis needs of modern astronomical surveys and the need to organize and reuse expensive derived data. The bie is the first platform for computational statistics designed explicitly to enable Bayesian update and model comparison for astronomical problems. Bayesian update is based on the representation of high-dimensional posterior distributions using metric-ball-tree based kernel density estimation. Among its algorithmic offerings, the bie emphasizes hybrid tempered Markov chain Monte Carlo schemes that robustly sample multimodal posterior distributions in high-dimensional parameter spaces. Moreover, the bie implements a full persistence or serialization system that stores the full byte-level image of the running inference and previously characterized posterior distributions for later use. Two new algorithms to compute the marginal likelihood from the posterior distribution, developed for and implemented in the bie, enable model comparison for complex models and data sets. Finally, the bie was designed to be a collaborative platform for applying Bayesian methodology to astronomy. It includes an extensible object-oriented and easily extended framework that implements every aspect of the Bayesian inference. By providing a variety of statistical algorithms for all phases of the inference problem, a scientist may explore a variety of approaches with a single model and data implementation. Additional technical details and download details are available from http://www.astro.umass.edu/bie. The bie is distributed under the GNU General Public License.

Keywords: methods: data analysis; methods: numerical; methods: statistical; astronomical data bases: miscellaneous; virtual observatory tools

Journal Article.  18747 words.  Illustrated.

Subjects: Astronomy and Astrophysics

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