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Activity Number:
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59
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Type:
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Topic Contributed
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Date/Time:
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Sunday, August 2, 2009 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Statistics and the Environment
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| Abstract - #304459 |
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Title:
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Putting Citizen Science to Work: Hierarchical Modeling, Data Mining, High-Dimensional MCMC, and the eBird Database
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Author(s):
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Benjamin Shaby*+
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Companies:
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Cornell University
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Address:
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Department of Statistical Science, Ithaca, NY, 14853-3801,
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Keywords:
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MCMC ; tapering ; citizen science ; high-dimensional
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Abstract:
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We use a hierarchical Bayesian model to describe avian count data contained in the massive eBird database, a citizen science initiative. This data set contains complex interactions and dependencies between many environmental covariates, precluding the use of traditional parametric regression models. In their place, we integrate "black box" data mining models into our sampler. In addition, the sheer volume of data renders traditional computational tools inadequate. To facilitate computations on this very high-dimensional data, we employ tapered quasi-Bayesian methods, as well as novel adaptive MCMC techniques, without which computations would be intractable.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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