Department of Psychological and Brain Sciences, Indiana University, 1101 E. 10th St., Bloomington, IN, 47405, USA.
Psychon Bull Rev. 2018 Feb;25(1):155-177. doi: 10.3758/s13423-017-1272-1.
This article explains the foundational concepts of Bayesian data analysis using virtually no mathematical notation. Bayesian ideas already match your intuitions from everyday reasoning and from traditional data analysis. Simple examples of Bayesian data analysis are presented that illustrate how the information delivered by a Bayesian analysis can be directly interpreted. Bayesian approaches to null-value assessment are discussed. The article clarifies misconceptions about Bayesian methods that newcomers might have acquired elsewhere. We discuss prior distributions and explain how they are not a liability but an important asset. We discuss the relation of Bayesian data analysis to Bayesian models of mind, and we briefly discuss what methodological problems Bayesian data analysis is not meant to solve. After you have read this article, you should have a clear sense of how Bayesian data analysis works and the sort of information it delivers, and why that information is so intuitive and useful for drawing conclusions from data.
本文使用几乎没有数学符号的方法解释了贝叶斯数据分析的基本概念。贝叶斯思想已经符合您从日常推理和传统数据分析中获得的直觉。本文提供了一些简单的贝叶斯数据分析示例，说明了如何直接解释贝叶斯分析提供的信息。本文还讨论了贝叶斯方法对零假设评估的作用。本文澄清了初学者在其他地方可能获得的关于贝叶斯方法的误解。我们讨论了先验分布，并解释了它们不是负担，而是重要的资产。我们讨论了贝叶斯数据分析与思维的贝叶斯模型之间的关系，并简要讨论了贝叶斯数据分析不打算解决的方法问题。阅读本文后，您应该清楚地了解贝叶斯数据分析的工作原理以及它提供的信息类型，以及为什么这些信息对于从数据中得出结论如此直观和有用。
