Abstract Details
Activity Number:
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613
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Type:
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Contributed
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Date/Time:
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Thursday, August 7, 2014 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Computing
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Abstract #312891
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View Presentation
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Title:
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Geometric Ergodicity of Bayesian Scale-Usage Models
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Author(s):
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Andrew Olsen*+ and Radu Herbei
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Companies:
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and Ohio State University
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Keywords:
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MCMC ;
Geometric Ergodicity ;
Scale-usage
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Abstract:
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Geometric ergodicity is a key property of Markov chains that is typically used for establishing corresponding central limit theorems. However, it is often incredibly challenging to verify, particularly for sophisticated applications. Bayesian scale-usage models are utilized in areas such as survey data where each individual has a unique ranking system that is comparable to other individuals only through shifting and scaling their latent responses. In this work we study the ergodic properties of Markov chains exploring the posterior distribution corresponding to a general class of scale-usage models. We show that for such applications, under certain conditions, typical MCMC samplers are geometrically ergodic.
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Authors who are presenting talks have a * after their name.
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