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使用EMC2包的贝叶斯层次认知建模
Niek Stevenson1, Michelle C Donzallaz2, Reilly J Innes2
1Department of Psychology, University of Amsterdam, Amsterdam, Netherlands. niek.stevenson@gmail.com.
本研究介绍了EMC2,这是一个R包,用于对认知选择模型的贝叶斯层次分析. 它简化了模型规范,估计,批评和推断,增强了认知建模工作流程.
科学领域:
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
- 贝叶斯统计学贝叶斯统计学
背景情况:
- 选择的认知模型对于理解决策至关重要.
- 贝叶斯层次分析为这些模型提供了一个强大的框架.
- 现有的工作流可以是复杂和计算密集的.
研究的目的:
- 介绍EMC2,一个新的R包用于对认知模型的贝叶斯层次分析.
- 提供全面的五阶段工作流程,简化认知模型分析.
- 为了促进复杂的认知模型的规范,估计,批评和推断.
主要方法:
- 开发EMC2 R包,使用五阶段工作流程.
- 对认知模型参数的线性模型规范的整合.
- 实施灵活的先验,层次结构和高效的抽样算法.
- 包括用于模型批评和推理的函数.
主要成果:
- 对于计算密集型认知模型,EMC2提供了一个用户友好的界面.
- 该包桥梁标准回归和认知建模.
- 使用两个证据积累模型证明工作流程.
结论:
- EMC2显著地简化并指导贝叶斯层次认知模型的分析.
- 该套件支持模型评估,改进,比较和解释.
- EMC2提高了先进的认知建模技术的可访问性和效率.
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