Home > Research > Publications & Outputs > Bayesian inference using artificial augmenting ...

Research

Associated organisational unit

Links

Text available via DOI:

Keywords

View graph of relations

Bayesian inference using artificial augmenting regressions

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

Published

Standard

Bayesian inference using artificial augmenting regressions. / Tsionas, Michael.
In: Communications in Statistics - Theory and Methods, Vol. 38, No. 9, 04.2009, p. 1361-1370.

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

Harvard

Tsionas, M 2009, 'Bayesian inference using artificial augmenting regressions', Communications in Statistics - Theory and Methods, vol. 38, no. 9, pp. 1361-1370. https://doi.org/10.1080/03610920802431044

APA

Tsionas, M. (2009). Bayesian inference using artificial augmenting regressions. Communications in Statistics - Theory and Methods, 38(9), 1361-1370. https://doi.org/10.1080/03610920802431044

Vancouver

Tsionas M. Bayesian inference using artificial augmenting regressions. Communications in Statistics - Theory and Methods. 2009 Apr;38(9):1361-1370. doi: 10.1080/03610920802431044

Author

Tsionas, Michael. / Bayesian inference using artificial augmenting regressions. In: Communications in Statistics - Theory and Methods. 2009 ; Vol. 38, No. 9. pp. 1361-1370.

Bibtex

@article{acdab3b4f77844fbacbb8dd3337ad080,
title = "Bayesian inference using artificial augmenting regressions",
abstract = "In this article, it is shown that many intractable problems of Bayesian inference can be cast in a form called “artificial augmenting regression” in which application of Markov Chain Monte Carlo techniques, especially Gibbs sampling with data augmentation, is rather convenient. The new techniques are illustrated using several challenging statistical problems and numerical results are presented.",
keywords = "Bayesian inference, Extreme values, Gibbs sampling, Markov Chain Monte Carlo, Pareto distributions, Regression , Stable distributions",
author = "Michael Tsionas",
year = "2009",
month = apr,
doi = "10.1080/03610920802431044",
language = "English",
volume = "38",
pages = "1361--1370",
journal = "Communications in Statistics - Theory and Methods",
issn = "0361-0926",
publisher = "Taylor and Francis Ltd.",
number = "9",

}

RIS

TY - JOUR

T1 - Bayesian inference using artificial augmenting regressions

AU - Tsionas, Michael

PY - 2009/4

Y1 - 2009/4

N2 - In this article, it is shown that many intractable problems of Bayesian inference can be cast in a form called “artificial augmenting regression” in which application of Markov Chain Monte Carlo techniques, especially Gibbs sampling with data augmentation, is rather convenient. The new techniques are illustrated using several challenging statistical problems and numerical results are presented.

AB - In this article, it is shown that many intractable problems of Bayesian inference can be cast in a form called “artificial augmenting regression” in which application of Markov Chain Monte Carlo techniques, especially Gibbs sampling with data augmentation, is rather convenient. The new techniques are illustrated using several challenging statistical problems and numerical results are presented.

KW - Bayesian inference

KW - Extreme values

KW - Gibbs sampling

KW - Markov Chain Monte Carlo

KW - Pareto distributions

KW - Regression

KW - Stable distributions

U2 - 10.1080/03610920802431044

DO - 10.1080/03610920802431044

M3 - Journal article

VL - 38

SP - 1361

EP - 1370

JO - Communications in Statistics - Theory and Methods

JF - Communications in Statistics - Theory and Methods

SN - 0361-0926

IS - 9

ER -