MCMC method for arbitrary missing patterns.
MCMC方法任意失踪模式。
During the process of Bayesian networks modeling, K2 and MCMC algorithms are utilized together.
在贝叶斯网络构造过程中，结合采用K2和MC - MC算法构建网络。
A novel method is proposed for digital modulation classification based on Markov chain Monte Carlo (MCMC).
提出了一种基于马尔可夫链蒙特卡罗(MCMC)的数字调制分类方法。
In this paper I suppose an MCMC random model generating procedure that can generate a model with the lowest criterion value.
本文提出了一个基于蒙特卡洛-马尔科夫链方法的随机模型生成方法，以产生准则函数值最小的备选模型。
In this article, three estimation methods, GMM, MCMC and EMM are studied. GMM is one of the earliest methods used in SV model and its character is simple;
本文重点讨论了广义矩估计法、马尔可夫链蒙特卡罗方法和有效矩估计法这三种各具特点的随机波动模型的参数估计方法。
At present, Modern Bayesian Statistics that is the represent of MCMC methodology is applied widely in almost all subjects and achieved marked achievements.
目前以MCMC方法为代表的现代贝叶斯统计学已广泛应用于几乎所有的学科，并取得了显著的成果。
Methods We applied generalized linear mixed models for the nuclear family data to set up the genetic variance component model and estimated parameters using MCMC.
将广义线性混合模型应用于核心家系资料建立遗传方差分量模型，运用MCMC方法进行参数估计。
Time variant motion model can increase the efficiency of the RJ-MCMC algorithm, reduce the number of ineffective particles, and enable it convergence to the real value faster.
时变的运动模型可以有效提高RJ -MCMC方法的效率，减少其无效的粒子点数，使其能更加快速地收敛到真实值。
Conclusion mi is able to solve a variety of problems in missing data sets and to improve the statistical power, especially with the use of MCMC method, for complicated missing data sets.
结论多重填补方法可以处理有缺失数据资料中的许多普遍问题，可提高统计效率，尤其是MCMC模型在处理复杂的缺失数据上，优势明显。
Given the observed hydrological data, the model can estimate the posterior probability distribution of each location of change-point by using the Monte Carlo Markov Chain (MCMC) sampling method.
该模型的核心部分是根据观测到的资料，通过蒙特卡洛马尔科夫链随机抽样的方法来估计变点位置的后验概率分布。
Given the observed hydrological data, the model can estimate the posterior probability distribution of each location of change-point by using the Monte Carlo Markov Chain (MCMC) sampling method.
该模型的核心部分是根据观测到的资料，通过蒙特卡洛马尔科夫链随机抽样的方法来估计变点位置的后验概率分布。
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