论文标题
纵向模型评估的贝叶斯因素通过功率后期评估
Bayes factors for longitudinal model assessment via power posteriors
论文作者
论文摘要
贝叶斯因子定义为两个竞争模型的边际似然函数的比率，是模型选择的天然贝叶斯程序。边际可能性通常是计算要求且复杂的。在线性混合模型（LMM）中，这种情况尤其繁琐，因为边际似然函数涉及由参数数量和随机效应的数量确定的大尺寸的积分，而随机效应的数量随着样本中的个体数量而增加。在马尔可夫链蒙特卡洛算法的背景下，功率后部是一个有吸引力的建议，该算法允许在单位范围内表达边际可能性为一维积分。本文探讨了在LMMS中使用电力后期的使用，并通过两项模拟研究和关于地中海欧洲沙丁鱼着陆的真实数据讨论其行为。
Bayes factor, defined as the ratio of the marginal likelihood functions of two competing models, is the natural Bayesian procedure for model selection. Marginal likelihoods are usually computationally demanding and complex. This scenario is particularly cumbersome in linear mixed models (LMMs) because marginal likelihood functions involve integrals of large dimensions determined by the number of parameters and the number of random effects, which in turn increase with the number of individuals in the sample. The power posterior is an attractive proposal in the context of the Markov chain Monte Carlo algorithms that allows expressing marginal likelihoods as one-dimensional integrals over the unit range. This paper explores the use of power posteriors in LMMs and discusses their behaviour through two simulation studies and a real data set on European sardine landings in the Mediterranean Sea.
