Opaque prior distributions in Bayesian latent variable models

IF 2 3区 心理学 Q2 PSYCHOLOGY, MATHEMATICAL
Edgar C. Merkle, Oludare Ariyo, Sonja D. Winter, Mauricio Garnier-Villarreal
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引用次数: 0

Abstract

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.

贝叶斯潜变量模型中的不透明先验分布
我们回顾了贝叶斯潜变量模型中的常见情况,其中研究人员指定的先验分布与估计过程中使用的先验分布不同。这些情况可能来自相关矩阵的正定要求,因子负载的符号不确定性,以及阈值参数的顺序约束。对于再现性和涉及先验分布(包括先验预测评估和贝叶斯因子)的模型检查来说,这个问题尤其成问题。在这些情况下,人们可能会评估错误的模型,从而对结果的相关性产生怀疑。这个问题最直接的解决方案有时涉及使用信息性先验分布。我们探索其他解决方案,并为实践提出建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.70
自引率
6.50%
发文量
16
审稿时长
36 weeks
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