University of California, Irvine, Irvine, CA, USA.
University of Amsterdam, Amsterdam, The Netherlands.
Psychon Bull Rev. 2018 Feb;25(1):219-234. doi: 10.3758/s13423-017-1317-5.
In this guide, we present a reading list to serve as a concise introduction to Bayesian data analysis. The introduction is geared toward reviewers, editors, and interested researchers who are new to Bayesian statistics. We provide commentary for eight recommended sources, which together cover the theoretical and practical cornerstones of Bayesian statistics in psychology and related sciences. The resources are presented in an incremental order, starting with theoretical foundations and moving on to applied issues. In addition, we outline an additional 32 articles and books that can be consulted to gain background knowledge about various theoretical specifics and Bayesian approaches to frequently used models. Our goal is to offer researchers a starting point for understanding the core tenets of Bayesian analysis, while requiring a low level of time commitment. After consulting our guide, the reader should understand how and why Bayesian methods work, and feel able to evaluate their use in the behavioral and social sciences.
在本指南中，我们提供了一份阅读清单，作为对贝叶斯数据分析的简明介绍。本介绍面向的是审稿人、编辑和对贝叶斯统计感兴趣但不熟悉该领域的研究人员。我们对推荐的 8 个资源进行了评论，这些资源涵盖了心理学和相关科学中贝叶斯统计的理论和实践基础。这些资源是按照递增的顺序呈现的，从理论基础开始，然后是应用问题。此外，我们还列出了另外 32 篇文章和书籍，以供参考，以了解各种理论细节和贝叶斯方法在常用模型中的应用。我们的目标是为研究人员提供一个理解贝叶斯分析核心原则的起点，同时要求投入的时间很少。在查阅我们的指南后，读者应该能够理解贝叶斯方法的工作原理以及为什么会这样，并能够评估它们在行为和社会科学中的使用。
