标题： 使用MCMC和重要性抽样的缺失数据模型比较 显示英文标题

标题： Model comparison with missing data using MCMC and importance sampling

摘要： 在竞争性统计模型之间进行选择是一个具有挑战性的问题，尤其是在竞争模型是非嵌套的情况下。 在本文中，我们通过设计一种结合MCMC和重要性抽样的算法，提供了一个简单的解决方案，以获得计算高效的边缘似然估计，这些估计可用于比较模型。 该算法成功应用于纵向流行病学和时间序列数据集，并显示出比现有方法在计算边缘似然方面更具优势。 显示英文摘要

摘要： Selecting between competing statistical models is a challenging problem especially when the competing models are non-nested. In this paper we offer a simple solution by devising an algorithm which combines MCMC and importance sampling to obtain computationally efficient estimates of the marginal likelihood which can then be used to compare the models. The algorithm is successfully applied to longitudinal epidemic and time series data sets and shown to outperform existing methods for computing the marginal likelihood.