Statistics – Computation
Scientific paper
Jan 2010
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010aas...21523101m&link_type=abstract
American Astronomical Society, AAS Meeting #215, #231.01; Bulletin of the American Astronomical Society, Vol. 36, p.1111
Statistics
Computation
Scientific paper
In this session we have seen some snapshots of the broad spectrum of challenges, in this age of huge, complex, computer-intensive models, data, instruments,and questions. These challenges bridge astronomy at many wavelengths; basic physics; machine learning; -- and statistics. At one end of our spectrum, we think of 'compressing' the data with non-parametric methods. This raises the question of creating 'pseudo-replicas' of the data for uncertainty estimates. What would be involved in, e.g. boot-strap and related methods? Somewhere in the middle are these non-parametric methods for encapsulating the uncertainty information. At the far end, we find more model-based approaches, with the physics model embedded in the likelihood and analysis. The other distinctive problem is really the 'black-box' problem, where one has a complicated e.g. fundamental physics-based computer code, or 'black box', and one needs to know how changing the parameters at input -- due to uncertainties of any kind -- will map to changing the output. All of these connect to challenges in complexity of data and computation speed. Dr. Meng will highlight ways to 'cut corners' with advanced computational techniques, such as Parallel Tempering and Equal Energy methods. As well, there are cautionary tales of running automated analysis with real data -- where "30 sigma" outliers due to data artifacts can be more common than the astrophysical event of interest.
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