贝叶斯因子
频数推理
贝叶斯统计
心理信息
贝叶斯概率
贝叶斯定理
集合(抽象数据类型)
频发概率
心理学
计算机科学
先验概率
贝叶斯法则
人工智能
数据科学
统计
数学
贝叶斯推理
梅德林
程序设计语言
法学
政治学
作者
Xenia Schmalz,José Biurrun Manresa,Lei Zhang
出处
期刊:Psychological Methods
[American Psychological Association]
日期:2021-11-15
卷期号:28 (3): 705-718
被引量:66
摘要
The use of Bayes factors is becoming increasingly common in psychological sciences. Thus, it is important that researchers understand the logic behind the Bayes factor in order to correctly interpret it, and the strengths of weaknesses of the Bayesian approach. As education for psychological scientists focuses on frequentist statistics, resources are needed for researchers and students who want to learn more about this alternative approach. The aim of the current article is to provide such an overview to a psychological researcher. We cover the general logic behind Bayesian statistics, explain how the Bayes factor is calculated, how to set the priors in popular software packages to reflect the prior beliefs of the researcher, and finally provide a set of recommendations and caveats for interpreting Bayes factors. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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