EM算法是近年来常用的求后验众数的估计的一种数据增广算法,但由于求出其E步中积分的显示表达式有时很困难,甚至不可能,限制了其应用的广泛性.而Monte Carlo EM算法很好地解决了这个问题,将EM算法中E步的积分用Monte Carlo模拟来有效实...EM算法是近年来常用的求后验众数的估计的一种数据增广算法,但由于求出其E步中积分的显示表达式有时很困难,甚至不可能,限制了其应用的广泛性.而Monte Carlo EM算法很好地解决了这个问题,将EM算法中E步的积分用Monte Carlo模拟来有效实现,使其适用性大大增强.但无论是EM算法,还是Monte Carlo EM算法,其收敛速度都是线性的,被缺损信息的倒数所控制,当缺损数据的比例很高时,收敛速度就非常缓慢.而Newton-Raphson算法在后验众数的附近具有二次收敛速率.本文提出Monte Carlo EM加速算法,将Monte Carlo EM算法与Newton-Raphson算法结合,既使得EM算法中的E步用Monte Carlo模拟得以实现,又证明了该算法在后验众数附近具有二次收敛速度.从而使其保留了Monte Carlo EM算法的优点,并改进了Monte Carlo EM算法的收敛速度.本文通过数值例子,将Monte Carlo EM加速算法的结果与EM算法、Monte Carlo EM算法的结果进行比较,进一步说明了Monte Carlo EM加速算法的优良性.展开更多
针对不规则区域面积测算中定位精度和面积计算精度两方面不足,提出一种定位精度高、面积误差小的面积测算新方法。其采用一种组合定位方法精确定位,即将差分GPS测量系统(DGPS)与马尔可夫链蒙特卡罗(Markov chain Monte Carol,MCMC)粒子...针对不规则区域面积测算中定位精度和面积计算精度两方面不足,提出一种定位精度高、面积误差小的面积测算新方法。其采用一种组合定位方法精确定位,即将差分GPS测量系统(DGPS)与马尔可夫链蒙特卡罗(Markov chain Monte Carol,MCMC)粒子滤波相结合,再配合复化Newton-cotes算法,拟合边界曲线并准确求得区域面积。将MCMC粒子滤波应用于DGPS定位数据处理,其既可处理非高斯分布噪声,又解决粒子滤波(PF)的粒子退化问题,提高定位精度。将复化Newton-cotes算法应用于面积计算,其既避免高次插值的舍入误差,又将面积区间进一步细分,提高面积计算精度。实验结果表明,该新方法定位精度更高,面积误差更小。展开更多
Generalized linear mixed models (GLMMs) are typically constructed by incorporating random effects into the linear predictor. The random effects are usually assumed to be normally distributed with mean zero and varianc...Generalized linear mixed models (GLMMs) are typically constructed by incorporating random effects into the linear predictor. The random effects are usually assumed to be normally distributed with mean zero and variance-covariance identity matrix. In this paper, we propose to release random effects to non-normal distributions and discuss how to model the mean and covariance structures in GLMMs simultaneously. Parameter estimation is solved by using Quasi-Monte Carlo (QMC) method through iterative Newton-Raphson (NR) algorithm very well in terms of accuracy and stabilization, which is demonstrated by real binary salamander mating data analysis and simulation studies.展开更多
文摘EM算法是近年来常用的求后验众数的估计的一种数据增广算法,但由于求出其E步中积分的显示表达式有时很困难,甚至不可能,限制了其应用的广泛性.而Monte Carlo EM算法很好地解决了这个问题,将EM算法中E步的积分用Monte Carlo模拟来有效实现,使其适用性大大增强.但无论是EM算法,还是Monte Carlo EM算法,其收敛速度都是线性的,被缺损信息的倒数所控制,当缺损数据的比例很高时,收敛速度就非常缓慢.而Newton-Raphson算法在后验众数的附近具有二次收敛速率.本文提出Monte Carlo EM加速算法,将Monte Carlo EM算法与Newton-Raphson算法结合,既使得EM算法中的E步用Monte Carlo模拟得以实现,又证明了该算法在后验众数附近具有二次收敛速度.从而使其保留了Monte Carlo EM算法的优点,并改进了Monte Carlo EM算法的收敛速度.本文通过数值例子,将Monte Carlo EM加速算法的结果与EM算法、Monte Carlo EM算法的结果进行比较,进一步说明了Monte Carlo EM加速算法的优良性.
文摘针对不规则区域面积测算中定位精度和面积计算精度两方面不足,提出一种定位精度高、面积误差小的面积测算新方法。其采用一种组合定位方法精确定位,即将差分GPS测量系统(DGPS)与马尔可夫链蒙特卡罗(Markov chain Monte Carol,MCMC)粒子滤波相结合,再配合复化Newton-cotes算法,拟合边界曲线并准确求得区域面积。将MCMC粒子滤波应用于DGPS定位数据处理,其既可处理非高斯分布噪声,又解决粒子滤波(PF)的粒子退化问题,提高定位精度。将复化Newton-cotes算法应用于面积计算,其既避免高次插值的舍入误差,又将面积区间进一步细分,提高面积计算精度。实验结果表明,该新方法定位精度更高,面积误差更小。
文摘Generalized linear mixed models (GLMMs) are typically constructed by incorporating random effects into the linear predictor. The random effects are usually assumed to be normally distributed with mean zero and variance-covariance identity matrix. In this paper, we propose to release random effects to non-normal distributions and discuss how to model the mean and covariance structures in GLMMs simultaneously. Parameter estimation is solved by using Quasi-Monte Carlo (QMC) method through iterative Newton-Raphson (NR) algorithm very well in terms of accuracy and stabilization, which is demonstrated by real binary salamander mating data analysis and simulation studies.
