We review common situations in Bayesian latent variable models where the prior distribution that a researcher specifies differs from the prior distribution used during estimation. These situations can arise from the positive definite requirement on correlation matrices, from sign indeterminacy of factor loadings, and from order constraints on threshold parameters. The issue is especially problematic for reproducibility and for model checks that involve prior distributions, including prior predictive assessment and Bayes factors. In these cases, one might be assessing the wrong model, casting doubt on the relevance of the results. The most straightforward solution to the issue sometimes involves use of informative prior distributions. We explore other solutions and make recommendations for practice.

翻译：我们回顾了贝叶斯潜变量模型中常见的若干情形，在这些情形下，研究者指定的先验分布与估计过程中实际使用的先验分布存在差异。此类差异可能源于对相关矩阵的正定性要求、因子载荷的符号不确定性，以及阈值参数的顺序约束。该问题对可重复性以及涉及先验分布的模型检验（包括先验预测评估和贝叶斯因子）尤其具有危害性。在这些情形中，研究者可能正在评估错误模型，从而对结果的相关性产生质疑。解决该问题最直接的方法有时涉及使用信息性先验分布。我们探讨了其他解决方案，并针对实际应用提出了相关建议。