Abstract Details
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Activity Number:
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414
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Type:
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Contributed
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Date/Time:
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Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #312545
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Title:
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Writing Error-Free MCMC Code
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Author(s):
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Margaret Short*+
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Companies:
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University of Alaska Fairbanks
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Keywords:
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error-free code ;
Markov chain Monte Carlo ;
non-ignorable missingness
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Abstract:
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When OpenBUGS can't fit a Bayesian model, usually because the model is too complex for it to handle, C++ or another mid-level programming language is an alternative. Verifiably error-free code would be ideal, but more realistically what I aim for is more robust code that is relatively easy to debug. I discuss what I've figured out over the years, and illustrate with a model for estimating salmon escapement using data that has non-ignorable missingness.
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Authors who are presenting talks have a * after their name.
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