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Convergence analysis of MCMC method ...

Fisher, Diana.

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Convergence analysis of MCMC method in the study of genetic linkage with missing data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Convergence analysis of MCMC method in the study of genetic linkage with missing data./
Author:	Fisher, Diana.
Description:	85 p.
Notes:	Source: Masters Abstracts International, Volume: 44-03, page: 1379.
Contained By:	Masters Abstracts International44-03.
Subject:	Mathematics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1430428
ISBN:	9780542419232

Convergence analysis of MCMC method in the study of genetic linkage with missing data.
Fisher, Diana.

Convergence analysis of MCMC method in the study of genetic linkage with missing data. - 85 p.

Source: Masters Abstracts International, Volume: 44-03, page: 1379.

Thesis (M.A.)--Marshall University, 2005.

Computational infeasibility of exact methods for solving genetic linkage analysis problems has led to the development of a new collection of stochastic methods, all of which require the use of Markov chains. The purpose of this work is to investigate the complexities of missing data in pedigree analysis using the Monte Carlo Markov Chain (MCMC) method as compared to exact results. We attempt to determine an association between missing data in a familial pedigree and the convergence to stationarity of a descent graph Markov chain implemented in the stochastic method for parametric linkage analysis. Using the method for maximum autocorrelation and bounding of the second largest eigenvalue, we will study the effects of missing data on the convergence rate and the accuracy of the MCMC method in solving the pedigree analysis problem. Finally, we will use the computational implementation of SimWalk2 to study the convergence rate and accuracy of the MCMC method for the disease Episodic Ataxia.

ISBN: 9780542419232Subjects--Topical Terms:

515831
Mathematics.

Convergence analysis of MCMC method in the study of genetic linkage with missing data.
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100 1 $a Fisher, Diana. $3 1912091
245 1 0 $a Convergence analysis of MCMC method in the study of genetic linkage with missing data.
300 $a 85 p.
500 $a Source: Masters Abstracts International, Volume: 44-03, page: 1379.
500 $a Chair: Alfred Akinsete.
502 $a Thesis (M.A.)--Marshall University, 2005.
520 $a Computational infeasibility of exact methods for solving genetic linkage analysis problems has led to the development of a new collection of stochastic methods, all of which require the use of Markov chains. The purpose of this work is to investigate the complexities of missing data in pedigree analysis using the Monte Carlo Markov Chain (MCMC) method as compared to exact results. We attempt to determine an association between missing data in a familial pedigree and the convergence to stationarity of a descent graph Markov chain implemented in the stochastic method for parametric linkage analysis. Using the method for maximum autocorrelation and bounding of the second largest eigenvalue, we will study the effects of missing data on the convergence rate and the accuracy of the MCMC method in solving the pedigree analysis problem. Finally, we will use the computational implementation of SimWalk2 to study the convergence rate and accuracy of the MCMC method for the disease Episodic Ataxia.
590 $a School code: 0817.
650 4 $a Mathematics. $3 515831
650 4 $a Biology, Genetics. $3 1017730
650 4 $a Biology, Molecular. $3 1017719
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690 $a 0369
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710 2 0 $a Marshall University. $3 1026833
773 0 $t Masters Abstracts International $g 44-03.
790 1 0 $a Akinsete, Alfred, $e advisor
790 $a 0817
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792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1430428