This journal recently published a systematic review of simulation studies on the performance of Bayesian approaches for estimating latent variable models in small samples. The authors of this review highlighted that Bayesian approaches can perform poorly (i.e., by exhibiting bias) when the prior distributions are not thoughtfully constructed on the basis of previous knowledge. In this comment, we question whether the bias is the most important criterion when the sample size is small. We argue that the variability is more important and should therefore not be ignored. Moreover, because one of the most important selling points of Bayesian approaches was not addressed in the article, we argue that although somewhat biased, Bayesian approaches allow for more accurate estimates (i.e., a smaller mean squared error) than Maximum Likelihood (ML) in small samples, and we show one such approach that is more accurate than ML.

该杂志最近发表了有关贝叶斯方法在小样本样本中估计潜在变量模型性能的模拟研究的系统综述。这篇评论的作者强调指出，如果先前的分布不是基于先前的知识而被深思熟虑的，贝叶斯方法可能会表现不佳（即表现出偏差）。在这篇评论中，我们质疑当样本量较小时偏差是否是最重要的标准。我们认为可变性更为重要，因此不应忽略。此外，由于本文未讨论贝叶斯方法最重要的卖点之一，因此我们认为，尽管有些偏颇，贝叶斯方法仍可实现更准确的估算（即，