Strategies for MCMC computation in quantitative genetics

Rasmus Waagepetersen; N. Ibánez; Daniel Sorensen

Strategies for MCMC computation in quantitative genetics

Rasmus Waagepetersen, N. Ibánez, Daniel Sorensen

Institut for Matematiske Fag

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

Abstract

Given observations of a trait and a pedigree for a group of animals, the basic model in quantitative genetics is a linear mixed model with genetic random effects. The correlation matrix of the genetic random effects is determined by the pedigree and is typically very highdimensional but with a sparse inverse. Maximum likelihood inference and Bayesian inference for the linear mixed model are well-studied topics (Sorensen and Gianola, 2002). Regarding Bayesian inference, with appropriate choice of priors, the full conditional distributions are standard distributions and Gibbs sampling can be implemented relatively straightforwardly.

Originalsprog	Engelsk
Titel	Proceedings of the 8th World Congress on Genetics applied to Livestock Production
Antal sider	6
Forlag	SBMA, Sociedade Brasileira de Melhoramento Animal
Publikationsdato	2006
ISBN (Elektronisk)	85600088016
Status	Udgivet - 2006
Begivenhed	World Congress on Genetics Applied to Livestock Production - Belo Horizonte, Brasilien Varighed: 13 aug. 2006 → 18 aug. 2006 Konferencens nummer: 8

Konference

Konference	World Congress on Genetics Applied to Livestock Production
Nummer	8
Land/Område	Brasilien
By	Belo Horizonte
Periode	13/08/2006 → 18/08/2006

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Citationsformater

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title = "Strategies for MCMC computation in quantitative genetics",

abstract = "Given observations of a trait and a pedigree for a group of animals, the basic model in quantitative genetics is a linear mixed model with genetic random effects. The correlation matrix of the genetic random effects is determined by the pedigree and is typically very highdimensional but with a sparse inverse. Maximum likelihood inference and Bayesian inference for the linear mixed model are well-studied topics (Sorensen and Gianola, 2002). Regarding Bayesian inference, with appropriate choice of priors, the full conditional distributions are standard distributions and Gibbs sampling can be implemented relatively straightforwardly.",

author = "Rasmus Waagepetersen and N. Ib{\'a}nez and Daniel Sorensen",

year = "2006",

language = "English",

booktitle = "Proceedings of the 8th World Congress on Genetics applied to Livestock Production",

publisher = "SBMA, Sociedade Brasileira de Melhoramento Animal",

note = "World Congress on Genetics Applied to Livestock Production ; Conference date: 13-08-2006 Through 18-08-2006",

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Strategies for MCMC computation in quantitative genetics. / Waagepetersen, Rasmus; Ibánez, N.; Sorensen, Daniel.
Proceedings of the 8th World Congress on Genetics applied to Livestock Production. SBMA, Sociedade Brasileira de Melhoramento Animal, 2006.

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

TY - GEN

T1 - Strategies for MCMC computation in quantitative genetics

AU - Waagepetersen, Rasmus

AU - Ibánez, N.

AU - Sorensen, Daniel

N1 - Conference code: 8

PY - 2006

Y1 - 2006

N2 - Given observations of a trait and a pedigree for a group of animals, the basic model in quantitative genetics is a linear mixed model with genetic random effects. The correlation matrix of the genetic random effects is determined by the pedigree and is typically very highdimensional but with a sparse inverse. Maximum likelihood inference and Bayesian inference for the linear mixed model are well-studied topics (Sorensen and Gianola, 2002). Regarding Bayesian inference, with appropriate choice of priors, the full conditional distributions are standard distributions and Gibbs sampling can be implemented relatively straightforwardly.

AB - Given observations of a trait and a pedigree for a group of animals, the basic model in quantitative genetics is a linear mixed model with genetic random effects. The correlation matrix of the genetic random effects is determined by the pedigree and is typically very highdimensional but with a sparse inverse. Maximum likelihood inference and Bayesian inference for the linear mixed model are well-studied topics (Sorensen and Gianola, 2002). Regarding Bayesian inference, with appropriate choice of priors, the full conditional distributions are standard distributions and Gibbs sampling can be implemented relatively straightforwardly.

M3 - Article in proceeding

BT - Proceedings of the 8th World Congress on Genetics applied to Livestock Production

PB - SBMA, Sociedade Brasileira de Melhoramento Animal

T2 - World Congress on Genetics Applied to Livestock Production

Y2 - 13 August 2006 through 18 August 2006

ER -

Strategies for MCMC computation in quantitative genetics

Abstract

Konference

AUB Link

Fingeraftryk

Citationsformater