Zusammenfassung
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.
I describe key concepts that illustrate the power of Bayesian inference to
address these needs and outline the computational challenge. The techniques
presented are based on experience gained in modelling star-counts and stellar
populations, analysing the morphology of galaxy images, and performing Bayesian
investigations of semi-analytic models of galaxy formation. These inference
problems require advanced Markov chain Monte Carlo (MCMC) algorithms that
expedite sampling, mixing, and the analysis of the Bayesian posterior
distribution. The BIE was designed to be a collaborative platform for applying
Bayesian methodology to astronomy. By providing a variety of statistical
algorithms for all phases of the inference problem, a user may explore a
variety of approaches with a single model implementation. Indeed, each of the
separate scientific investigations above has benefited from the solutions posed
for the other investigations, and I anticipate that the same solutions will be
of general value for other areas of astronomical research. Finally, to protect
one's computational investment against loss any equipment failure and human
error, the BIE includes a comprehensive persistence system that enables
byte-level checkpointing and restoration.
Nutzer