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A case study in non-centering for data augmentation: stochastic epidemics

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A case study in non-centering for data augmentation: stochastic epidemics. / Neal, Peter John; Roberts, Gareth .
In: Statistics and Computing, Vol. 15, No. 4, 2005, p. 315-327.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Neal, PJ & Roberts, G 2005, 'A case study in non-centering for data augmentation: stochastic epidemics', Statistics and Computing, vol. 15, no. 4, pp. 315-327. https://doi.org/10.1007/s11222-005-4074-7

APA

Neal, P. J., & Roberts, G. (2005). A case study in non-centering for data augmentation: stochastic epidemics. Statistics and Computing, 15(4), 315-327. https://doi.org/10.1007/s11222-005-4074-7

Vancouver

Neal PJ, Roberts G. A case study in non-centering for data augmentation: stochastic epidemics. Statistics and Computing. 2005;15(4):315-327. doi: 10.1007/s11222-005-4074-7

Author

Neal, Peter John ; Roberts, Gareth . / A case study in non-centering for data augmentation : stochastic epidemics. In: Statistics and Computing. 2005 ; Vol. 15, No. 4. pp. 315-327.

Bibtex

@article{93e7c353b3974e2081f334e21cb9fb7b,
title = "A case study in non-centering for data augmentation: stochastic epidemics",
abstract = "In this paper, we introduce non-centered and partially non-centered MCMC algorithms for stochastic epidemic models. Centered algorithms previously considered in the literature perform adequately well for small data sets. However, due to the high dependence inherent in the models between the missing data and the parameters, the performance of the centered algorithms gets appreciably worse when larger data sets are considered. Therefore non-centered and partially non-centered algorithms are introduced and are shown to out perform the existing centered algorithms.",
keywords = "stochastic epidemic models, bernoulli random graphs, non-centered and partially non-centered MCMC algorithms, data augmentation",
author = "Neal, {Peter John} and Gareth Roberts",
year = "2005",
doi = "10.1007/s11222-005-4074-7",
language = "English",
volume = "15",
pages = "315--327",
journal = "Statistics and Computing",
issn = "0960-3174",
publisher = "Springer Netherlands",
number = "4",

}

RIS

TY - JOUR

T1 - A case study in non-centering for data augmentation

T2 - stochastic epidemics

AU - Neal, Peter John

AU - Roberts, Gareth

PY - 2005

Y1 - 2005

N2 - In this paper, we introduce non-centered and partially non-centered MCMC algorithms for stochastic epidemic models. Centered algorithms previously considered in the literature perform adequately well for small data sets. However, due to the high dependence inherent in the models between the missing data and the parameters, the performance of the centered algorithms gets appreciably worse when larger data sets are considered. Therefore non-centered and partially non-centered algorithms are introduced and are shown to out perform the existing centered algorithms.

AB - In this paper, we introduce non-centered and partially non-centered MCMC algorithms for stochastic epidemic models. Centered algorithms previously considered in the literature perform adequately well for small data sets. However, due to the high dependence inherent in the models between the missing data and the parameters, the performance of the centered algorithms gets appreciably worse when larger data sets are considered. Therefore non-centered and partially non-centered algorithms are introduced and are shown to out perform the existing centered algorithms.

KW - stochastic epidemic models

KW - bernoulli random graphs

KW - non-centered and partially non-centered MCMC algorithms

KW - data augmentation

U2 - 10.1007/s11222-005-4074-7

DO - 10.1007/s11222-005-4074-7

M3 - Journal article

VL - 15

SP - 315

EP - 327

JO - Statistics and Computing

JF - Statistics and Computing

SN - 0960-3174

IS - 4

ER -