Use this URL to cite or link to this record in EThOS:
Title: Bayesian methods in the selection of farm animals for breeding
Author: Firat, Mehmet Ziya
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 1995
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Please try the link below.
Access from Institution:
The purpose of this thesis is to implement Bayesian methods to solve theoretical and practical statistical problems in the selection of animals for breeding. The thesis is therefore mainly on the calculation of posterior distributions of variance components and functions of them, and the construction of optimum Bayesian selection methods for a single quantitative trait and multiple traits. Half-sib family structures are considered throughout, although the theory considered is more general in its application. Conventional and Bayesian methods for variance components estimation are reviewed from an animal breeding point of view, with emphasis on balanced data, but unbalanced data are also discussed. In Bayesian statistics the necessary integrations in several dimensions are usually difficult to perform by analytical means. A Gibbs sampling approach, which yields output readily translated into required inference summaries, is applied to integrations using suitable families of prior distributions. Gibbs sampling output is then used to develop appropriate graphical methods for summarising posterior distributions of genetic and phenotypic parameters, and to calculate the posterior expectations of breeding values and the expected progress using different selection procedures. The selection of farm animals for breeding is treated as a decision problem in which the utility of choosing a given number of individuals is assumed to be proportional to the sum of the corresponding breeding values. The Bayesian selection procedure in this case is contrasted with conventional procedures based on point estimates of parameters including a method based on modified parameter estimates known as bending.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available