Over the past 2 decades Bayesian methods have been gaining popularity in many scientific disciplines. However, to this date, they are rarely part of formal graduate statistical training in clinical science. Although Bayesian methods can be an attractive alternative to classical methods for answering certain research questions, they involve a heavy "overhead" (e.g., advanced mathematical methods, complex computations), which pose significant barriers to researchers interested in adding Bayesian methods to their statistical toolbox. To increase the accessibility of Bayesian methods for psychopathology researchers, this article presents a gentle introduction of the Bayesian inference framework and a tutorial on implementation. We first provide a primer on the key concepts of Bayesian inference and major implementation considerations related to Bayesian estimation. We then demonstrate how to apply hierarchical Bayesian modeling (HBM) to experimental psychopathology data. Using a real dataset collected from two clinical groups (schizophrenia and bipolar disorder) and a healthy comparison sample on a psychophysical gaze perception task, we illustrate how to model individual responses and group differences with probability functions respectful of the presumed underlying data-generating process and the hierarchical nature of the data. We provide the code with explanations and the data used to generate and visualize the results to facilitate learning. Finally, we discuss interpretation of the results in terms of posterior probabilities and compare the results with those obtained using a traditional method. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

中文翻译：

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将分层贝叶斯模型应用于实验精神病理学数据：介绍和教程。

在过去的 2 年里，贝叶斯方法在许多科学学科中越来越受欢迎。然而，迄今为止，他们很少是临床科学正式研究生统计培训的一部分。尽管贝叶斯方法可以成为回答某些研究问题的经典方法的有吸引力的替代方案，但它们涉及沉重的“开销”（例如，高级数学方法、复杂计算），这对有兴趣将贝叶斯方法添加到他们的统计工具箱中的研究人员构成了重大障碍. 为了增加精神病理学研究人员对贝叶斯方法的可及性，本文简要介绍了贝叶斯推理框架和实施教程。我们首先提供有关贝叶斯推理的关键概念和与贝叶斯估计相关的主要实施注意事项的入门知识。然后，我们演示如何将分层贝叶斯模型 (HBM) 应用于实验精神病理学数据。使用从两个临床组（精神分裂症和双相情感障碍）收集的真实数据集和关于心理物理凝视感知任务的健康比较样本，我们说明了如何使用尊重假定的潜在数据生成过程的概率函数对个体反应和组差异进行建模和数据的层次性质。我们为代码提供解释以及用于生成和可视化结果以促进学习的数据。最后，我们讨论后验概率对结果的解释，并将结果与​​使用传统方法获得的结果进行比较。（PsycInfo 数据库记录 (c) 2021 APA，保留所有权利）。