High-performance Computational Bayesian Inference for Astronomy

University Of Massachusetts Amherst, Amherst MA

Investigators

Abstract

Bayesian analysis has the power to combine information efficiently from diverse sources, helping to provide confidence bounds on theoretical models and methods for both model selection and hypothesis testing. This project brings state-of-the-art statistical and computational algorithms to bear on a set of data and theory comparison in astronomy. The wok bears on a variety of problems in other sciences, helping to fill the need for optimized data-mining tools. The system can be controlled and operated remotely and collaboratively. The introduction of a persistence system will permit saving of both data and metadata, making it accessible for future use. The statistical approach make the most efficient use of all the available information and permit the testing of hypotheses directly. Bayesian statistical models provide maximum flexibility in incorporating and using all the information, making a virtual observatory test-bed. Multi-disciplinary training opportunities are provided for graduate students and the work disseminated at both meetings, training sessions and for individual users through downloads.