神经科学中的实际贝叶斯推理:或者我如何学会停止担心并拥抱分布
Brandon S Coventry1, Edward L Bartlett2
1Department of Neurological Surgery and the Wisconsin Institute for Translational Neuroengineering, University of Wisconsin-Madison, Madison, WI USA 53705.
bioRxiv : the preprint server for biology
|December 4, 2023
概括
贝叶斯推理为生物科学中传统的零假设测试提供了一个更易于解释的替代方案. 本教程展示了它在神经科学中的应用,为强大的数据分析提供了工具.
科学领域:
- 神经科学是一个神经科学.
- 统计 统计 统计 统计
- 计算生物学 计算生物学
背景情况:
- 生物科学中的复制危机凸显了传统的零假设显著性测试 (NHST) 的问题.
- P值和NHST设计带来了解释挑战,影响了研究的可复制性.
- 贝叶斯推理因其可解释性和明确的先前假设而成为一个可行的替代方案.
结论:
- 贝叶斯推理为神经科学中的统计分析提供了一种更易于解释的方法.
- 这项工作使研究人员能够采用先进的统计方法,以获得更可靠的结果.
- 鼓励使用贝叶斯工具来加强生物研究中的数据分析.
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