可逆跳MCMC抽样在生物易感模式中的应用

Application in Biological Susceptibility Patterns of the Reversible Jump Markov Chain Monte Carlo Sampling

在一些生物实验中,受试者对于一定处理的易感模式往往是不同的,需要对不同的易感模式进行确定.由于易感模式的个数和形式同时未知,我们利用可逆跳MCMC方法,对易感模式个数和参数联合起来建模,基于他们的后验概率进行推断.为了方便,一般假定不同易感模式的分散程度是相同的.本文对分散程度不同的情况作了分析.数据分析表明可逆跳MCMC方法在易感模式处理中是非常显著的.

In some biological experiments, it is quite common that laboratory subjects may be different in their patterns of susceptibility to a treatment. We need to determine the different patterns of susceptibility. In this paper we model the number of susceptibility＇s patterns and the parameters jointly, and base inference about these quantities on their posterior probabilities, making use of reversible jump Markov chain Monte Carlo methods that are capable of jumping between the parameter subspaces corresponding to different numbers of components in the mixture. For convenience, we always assume different patterns of susceptibility have common variances. The paper apply the methodology to the analysis of univariate normal mixtures with different variances. The practical significance of the proposed method is illustrated with a dose-response data set.