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  • Note on Posterior Inference for the Bingham Distribution

    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in Communication in Statistics - Theory and Methods on 30/06/2017, available online: http://www.tandfonline.com/10.1080/03610926.2017.1346805

    Accepted author manuscript, 591 KB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

  • Note on the Bingham distribution

    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in Communication in Statistics - Theory and Methods on 30/06/2017, available online: http://www.tandfonline.com/10.1080/03610926.2017.1346805

    Accepted author manuscript, 248 KB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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Note on Posterior Inference for the Bingham Distribution

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

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Note on Posterior Inference for the Bingham Distribution. / Tsionas, Efthymios.
In: Communications in Statistics - Theory and Methods, Vol. 47, No. 12, 2018, p. 3022-3028.

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

Harvard

Tsionas, E 2018, 'Note on Posterior Inference for the Bingham Distribution', Communications in Statistics - Theory and Methods, vol. 47, no. 12, pp. 3022-3028. https://doi.org/10.1080/03610926.2017.1346805

APA

Tsionas, E. (2018). Note on Posterior Inference for the Bingham Distribution. Communications in Statistics - Theory and Methods, 47(12), 3022-3028. https://doi.org/10.1080/03610926.2017.1346805

Vancouver

Tsionas E. Note on Posterior Inference for the Bingham Distribution. Communications in Statistics - Theory and Methods. 2018;47(12):3022-3028. Epub 2017 Jun 30. doi: 10.1080/03610926.2017.1346805

Author

Tsionas, Efthymios. / Note on Posterior Inference for the Bingham Distribution. In: Communications in Statistics - Theory and Methods. 2018 ; Vol. 47, No. 12. pp. 3022-3028.

Bibtex

@article{12344b3cf6134af3bfce1c709486aa9f,
title = "Note on Posterior Inference for the Bingham Distribution",
abstract = "The properties of high-dimensional Bingham distributions have been studied by Kume and Walker. Fallaize and Kypraios propose Bayesian inference for the Bingham distribution and they use developments in Bayesian computation for distributions with doubly intractable normalising constants (M{\o}ller et al. 2006 ; Murray et al. 2006. However, they rely heavily on two Metropolis updates that they need to tune. In this paper we propose instead model selection with the marginal likelihood.",
keywords = "Bingham distribution, Bayesian, Markov Chain Monte Carlo, marginal likelihood",
author = "Efthymios Tsionas",
year = "2018",
doi = "10.1080/03610926.2017.1346805",
language = "English",
volume = "47",
pages = "3022--3028",
journal = "Communications in Statistics - Theory and Methods",
issn = "0361-0926",
publisher = "Taylor and Francis Ltd.",
number = "12",

}

RIS

TY - JOUR

T1 - Note on Posterior Inference for the Bingham Distribution

AU - Tsionas, Efthymios

PY - 2018

Y1 - 2018

N2 - The properties of high-dimensional Bingham distributions have been studied by Kume and Walker. Fallaize and Kypraios propose Bayesian inference for the Bingham distribution and they use developments in Bayesian computation for distributions with doubly intractable normalising constants (Møller et al. 2006 ; Murray et al. 2006. However, they rely heavily on two Metropolis updates that they need to tune. In this paper we propose instead model selection with the marginal likelihood.

AB - The properties of high-dimensional Bingham distributions have been studied by Kume and Walker. Fallaize and Kypraios propose Bayesian inference for the Bingham distribution and they use developments in Bayesian computation for distributions with doubly intractable normalising constants (Møller et al. 2006 ; Murray et al. 2006. However, they rely heavily on two Metropolis updates that they need to tune. In this paper we propose instead model selection with the marginal likelihood.

KW - Bingham distribution

KW - Bayesian

KW - Markov Chain Monte Carlo

KW - marginal likelihood

U2 - 10.1080/03610926.2017.1346805

DO - 10.1080/03610926.2017.1346805

M3 - Journal article

VL - 47

SP - 3022

EP - 3028

JO - Communications in Statistics - Theory and Methods

JF - Communications in Statistics - Theory and Methods

SN - 0361-0926

IS - 12

ER -