Abstract Details
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Activity Number:
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539
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Type:
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Invited
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Date/Time:
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Wednesday, August 6, 2014 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Physical and Engineering Sciences
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Abstract #310604
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View Presentation
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Title:
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Approximate Bayesian Computation for Kepler Data
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Author(s):
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Jessi Cisewski*+ and Megan Shabram and Eric B. Ford and Chad Schafer
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Companies:
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Carnegie Mellon and University of Florida and Penn State and Carnegie Mellon
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Keywords:
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approximate Bayesian computation ;
astrostatistics ;
likeihood-free ;
computational statistics ;
exoplanets ;
Kepler
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Abstract:
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Approximate Bayesian computation (ABC) provides a way to do inference on problems where a likelihood cannot be written down or is computationally intractable. Due to the many complexities of astronomical observations, ABC provides a nice framework for inference, but it must be used with care. Extra-solar planets, or exoplanets, are planets orbiting stars outside our solar system. I will motivate the appeal for ABC in the exoplanet setting, and discuss an application of ABC to eccentricity distributions of exoplanets using Kepler data.
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Authors who are presenting talks have a * after their name.
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