Journal article
The Hamming Ball Sampler
- Abstract:
- We introduce the Hamming ball sampler, a novel Markov chain Monte Carlo algorithm, for efficient inference in statistical models involving high-dimensional discrete state spaces. The sampling scheme uses an auxiliary variable construction that adaptively truncates the model space allowing iterative exploration of the full model space. The approach generalizes conventional Gibbs sampling schemes for discrete spaces and provides an intuitive means for user-controlled balance between statistical efficiency and computational tractability. We illustrate the generic utility of our sampling algorithm through application to a range of statistical models. Supplementary materials for this article are available online.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 1.7MB, Terms of use)
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- Publisher copy:
- 10.1080/01621459.2016.1222288
Authors
- Publisher:
- Taylor and Francis
- Journal:
- Journal of the American Statistical Association More from this journal
- Publication date:
- 2017-07-01
- Acceptance date:
- 2016-09-03
- DOI:
- EISSN:
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1537-274X
- ISSN:
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0162-1459
- Keywords:
- Pubs id:
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pubs:710435
- UUID:
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uuid:ad11233a-5872-4f9d-98fd-747db4c91c77
- Local pid:
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pubs:710435
- Source identifiers:
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710435
- Deposit date:
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2018-01-25
- ARK identifier:
Terms of use
- Copyright holder:
- Titsias and Yau
- Copyright date:
- 2017
- Notes:
- © 2017 Michalis K. Titsias and Christopher Yau. Published with license by Taylor and Francis. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Licence:
- CC Attribution (CC BY)
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