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相信什么:数据分析的贝叶斯方法。

What to believe: Bayesian methods for data analysis.

机构信息

Department of Psychological and Brain Sciences, Indiana University, 1101 E. 10th St., Bloomington, IN 47405-7007, USA.

出版信息

Trends Cogn Sci. 2010 Jul;14(7):293-300. doi: 10.1016/j.tics.2010.05.001. Epub 2010 Jun 11.

Abstract

Although Bayesian models of mind have attracted great interest from cognitive scientists, Bayesian methods for data analysis have not. This article reviews several advantages of Bayesian data analysis over traditional null-hypothesis significance testing. Bayesian methods provide tremendous flexibility for data analytic models and yield rich information about parameters that can be used cumulatively across progressive experiments. Because Bayesian statistical methods can be applied to any data, regardless of the type of cognitive model (Bayesian or otherwise) that motivated the data collection, Bayesian methods for data analysis will continue to be appropriate even if Bayesian models of mind lose their appeal.

摘要

虽然基于贝叶斯理论的心智模型吸引了认知科学家们的极大兴趣,但贝叶斯数据分析方法却并未受到关注。本文回顾了贝叶斯数据分析相对于传统的零假设检验的几个优势。贝叶斯方法为数据分析模型提供了极大的灵活性,并能提供有关参数的丰富信息,这些信息可以在渐进式实验中累积使用。由于贝叶斯统计方法可以应用于任何数据,而不管促使数据收集的认知模型(贝叶斯或其他类型)如何,因此,即使基于贝叶斯理论的心智模型失去吸引力,贝叶斯数据分析方法也将继续适用。

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