论文标题
马尔可夫队列状态转变模型：多项式分布表示
Markov cohort state-transition model: A multinomial distribution representation
论文作者
论文摘要
马尔可夫队列国家转变模型一直是模拟患者预后或更普遍的个体生活轨迹的标准方法。当前使用蒙特卡洛采样或主方程表示估算马尔可夫模型方差的方法在计算上很昂贵，并且在分析上很难表达和求解。我们以多项式分布的形式介绍了马尔可夫模型的替代表示。我们从原理中得出这种表示，然后在模拟练习中验证其真实性。该表示形式提供了一种精确而快速的方法来计算贝叶斯环境中估计过渡概率的方法。
Markov cohort state-transition models have been the standard approach for simulating the prognosis of patients or, more generally, the life trajectories of individuals over a time period. Current approaches for estimating the variance of a Markov model using a Monte Carlo sampling or a master equation representation are computationally expensive and analytically difficult to express and solve. We introduce an alternative representation of a Markov model in the form of a multinomial distribution. We derive this representation from principles and then verify its veracity in a simulation exercise. This representation provides an exact and fast approach to compute the variance and a way to estimate transition probabilities in a Bayesian setting.
