论文标题
RPEM：随机蒙特卡洛参数期望最大化算法
RPEM: Randomized Monte Carlo Parametric Expectation Maximization Algorithm
论文作者
论文摘要
我们始终使用无偏估计量并使用大都会算法同时使用离散和连续变量来启发。我们将其命名为随机参数期望最大化（RPEM）。特别是，我们将RPEM与Monolix的SAEM和Certara的QRPEM进行了比较，以现实的两室voriconazole模型与普通微分方程（ODE）和使用模拟数据进行了比较。我们表明，RPEM比SAEM和QRPEM快3至4倍，并且比重建人口参数更准确。
Inspired from quantum Monte Carlo, by using unbiased estimators all the time and sampling discrete and continuous variables at the same time using Metropolis algorithm, we present a novel, fast, and accurate high performance Monte Carlo Parametric Expectation Maximization (MCPEM) algorithm. We named it Randomized Parametric Expectation Maximization (RPEM). In particular, we compared RPEM with Monolix's SAEM and Certara's QRPEM for a realistic two-compartment Voriconazole model with ordinary differential equations (ODEs) and using simulated data. We show that RPEM is 3 to 4 times faster than SAEM and QRPEM, and more accurate than them in reconstructing the population parameters.
