Computational statistics using the Bayesian Inference Engine

Abstract

This paper introduces the Bayesian Inference Engine (BIE), a general parallel, optimised software package for parameter inference and model selection. This package is motivated by the analysis needs of modern astronomical surveys and the need to organise 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 emphasises hybrid tempered MCMC schemes that robustly sample multimodal posterior distributions in high-dimensional parameter spaces. Moreover, the BIE is implements a full persistence or serialisation system that stores the full byte-level image of the running inference and previously characterised 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 GPL.

Resubmitted version. Additional technical details and download details are available from http://www.astro.umass.edu/bie. The BIE is distributed under the GNU GPL

References in corpus (1)

• Bayesian inference of galaxy formation from the K-band luminosity function of galaxies: tensions between theory and observation

Cited by in corpus (7)

• The COS Absorption Survey of Baryon Harbors: Unveiling the Physical Conditions of Circumgalactic Gas through Multiphase Bayesian Ionization Modeling
• Building a Predictive Model of Galaxy Formation - I: Phenomenological Model Constrained to the $z=0$ Stellar Mass Function
• Connecting the Milky Way potential profile to the orbital timescales and spatial structure of the Sagittarius Stream
• SATMC: Spectral Energy Distribution Analysis Through Markov Chains
• Bayesian inferences of galaxy formation from the K-band luminosity and HI mass functions of galaxies: constraining star formation and feedback
• Dynamic real-time risk analytics of uncontrollable states in complex internet of things systems, cyber risk at the edge
• Informative Bayesian model selection for RR Lyrae star classifiers