Abstract Details
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Activity Number:
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619
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Type:
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Invited
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Date/Time:
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Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
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Sponsor:
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Statistical and Applied Mathematical Sciences Institute
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| Abstract - #307246 |
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Title:
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Statistical Methods in Astronomy
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Author(s):
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Tamas Budavari*+
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Companies:
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Johns Hopkins University
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Keywords:
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massive datasets ;
randomized algorithms ;
streaming ;
robust principal components ;
computational methods ;
astronomy
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Abstract:
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Astronomy is increasingly limited by our ability to understand the output of our instruments. The exponential growth in the data volume poses difficulties in every step: too much to move, to store, to analyze and to visualize. The emerging, large data sets provide us with new scientific opportunities but the interesting, subtle effects can only be discovered and understood by a thorough statistical approach that is also scalable to big data. From Bayesian modeling to streaming and randomized methods, astronomers are constantly searching for better solutions. This presentation will discuss the current trends and some of the recent developments in preparation for the next-generation surveys.
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Authors who are presenting talks have a * after their name.
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