東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Convergence analysis of MCMC method ...

Fisher, Diana.

FindBook

Google Book

Amazon

博客來

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Convergence analysis of MCMC method in the study of genetic linkage with missing data./
作者:	Fisher, Diana.
面頁冊數:	85 p.
附註:	Source: Masters Abstracts International, Volume: 44-03, page: 1379.
Contained By:	Masters Abstracts International44-03.
標題:	Mathematics. -
電子資源:	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.
LDR:01890nmm 2200289 4500 001 1822964
005 20061127103256.5
008 130610s2005 eng d
020 $a 9780542419232
035 $a (UnM)AAI1430428
035 $a AAI1430428
040 $a UnM $c UnM
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
690 $a 0405
690 $a 0369
690 $a 0307
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
791 $a M.A.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1430428