It is a challenging issue to map Quantitative Trait Loci (QTL) underlying complex discrete traits, which usually show discontinuous distribution; less information, using conventional statistical methods. Bayesian-Mark...It is a challenging issue to map Quantitative Trait Loci (QTL) underlying complex discrete traits, which usually show discontinuous distribution; less information, using conventional statistical methods. Bayesian-Markov chain Monte Carlo (Bayesian-MCMC) approach is the key procedure in mapping QTL for complex binary traits, which provides a complete posterior distribution for QTL parameters using all prior information. As a consequence, Bayesian estimates of all interested variables can be obtained straightforwardly basing on their posterior samples simulated by the MCMC algorithm. In our study, utilities of Bayesian-MCMC are demonstrated using simulated several animal outbred full-sib families with different family structures for a complex binary trait underlied by both a QTL; polygene. Under the Identity-by-Descent-Based variance component random model, three samplers basing on MCMC, including Gibbs sampling, Metropolis algorithm; reversible jump MCMC, were implemented to generate the joint posterior distribution of all unknowns so that the QTL parameters were obtained by Bayesian statistical inferring. The results showed that Bayesian-MCMC approach could work well; robust under different family structures; QTL effects. As family size increases; the number of family decreases, the accuracy of the parameter estimates will be improved. When the true QTL has a small effect, using outbred population experiment design with large family size is the optimal mapping strategy.展开更多
研究微波成像问题:应用贝叶斯方法将关于被测物体介电常数分布的先验信息描述为先验概率密度,结合散射场测量信息后,得到包含被测物体综合信息的后验概率密度,用马尔可夫链蒙特卡罗法(M CM C)-G ibbs抽样器来抽样后验概率密度,并用样本...研究微波成像问题:应用贝叶斯方法将关于被测物体介电常数分布的先验信息描述为先验概率密度,结合散射场测量信息后,得到包含被测物体综合信息的后验概率密度,用马尔可夫链蒙特卡罗法(M CM C)-G ibbs抽样器来抽样后验概率密度,并用样本均值作为对介电常数分布的估计。对介电常数呈“块状”分布物体进行的模拟成像结果表明:该方法对先验信息进行了有效利用,具有可行性和极强的抗噪声能力。该方法的特点还包括:可以方便地、易于控制地描述(明确或非明确)先验信息;可以给出问题的“完全”解,即任意一介电常数分布出现的概率。展开更多
基金supported by the National Natural Science Foundation of China(Grant No.30430500).
文摘It is a challenging issue to map Quantitative Trait Loci (QTL) underlying complex discrete traits, which usually show discontinuous distribution; less information, using conventional statistical methods. Bayesian-Markov chain Monte Carlo (Bayesian-MCMC) approach is the key procedure in mapping QTL for complex binary traits, which provides a complete posterior distribution for QTL parameters using all prior information. As a consequence, Bayesian estimates of all interested variables can be obtained straightforwardly basing on their posterior samples simulated by the MCMC algorithm. In our study, utilities of Bayesian-MCMC are demonstrated using simulated several animal outbred full-sib families with different family structures for a complex binary trait underlied by both a QTL; polygene. Under the Identity-by-Descent-Based variance component random model, three samplers basing on MCMC, including Gibbs sampling, Metropolis algorithm; reversible jump MCMC, were implemented to generate the joint posterior distribution of all unknowns so that the QTL parameters were obtained by Bayesian statistical inferring. The results showed that Bayesian-MCMC approach could work well; robust under different family structures; QTL effects. As family size increases; the number of family decreases, the accuracy of the parameter estimates will be improved. When the true QTL has a small effect, using outbred population experiment design with large family size is the optimal mapping strategy.
文摘研究微波成像问题:应用贝叶斯方法将关于被测物体介电常数分布的先验信息描述为先验概率密度,结合散射场测量信息后,得到包含被测物体综合信息的后验概率密度,用马尔可夫链蒙特卡罗法(M CM C)-G ibbs抽样器来抽样后验概率密度,并用样本均值作为对介电常数分布的估计。对介电常数呈“块状”分布物体进行的模拟成像结果表明:该方法对先验信息进行了有效利用,具有可行性和极强的抗噪声能力。该方法的特点还包括:可以方便地、易于控制地描述(明确或非明确)先验信息;可以给出问题的“完全”解,即任意一介电常数分布出现的概率。
