用回归器进行证据积累模型的贝叶斯推理.
Viet Hung Dao1, David Gunawan2, Robert Kohn1
1Australian School of Business, University of New South Wales.
Psychological methods
|February 13, 2025
概括
新的等级贝叶斯方法增强了LBA和DDM等证据积累模型 (EAM). 这些先进的技术有效地将决策共变量与模型参数联系起来,即使是大型数据集.
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.
- 心理测量 心理测量 心理测量
背景情况:
- 证据积累模型 (EAMs) 对于分析决策数据至关重要,但现有的等级贝叶斯框架对LBA和DDM等模型有局限性.
- 这些局限性包括大样本大小的可扩展性问题,复杂的模型以及将共变量与参数联系起来的困难.
研究的目的:
- 为线性弹道积累器 (LBA) 和扩散决策模型 (DDM) 开发先进的等级贝叶斯估计方法.
- 将相关的随机效应和对决策相关的共变量进行回归链接纳入这些模型中.
- 提供精确的 (基于粒子的MCMC) 和近似的 (波动贝叶斯式) 推理方法,以改进估计.
主要方法:
- 扩展等级贝叶斯框架,包括参与者之间的相关随机效应.
- 综合回归模型链接将与决策相关的共变量 (人或决策特定) 与模型参数连接起来.
- 使用基于粒子的马尔科夫链蒙特卡洛 (MCMC) 和近似变量贝叶斯 (VB) 方法实现了精确的贝叶斯推理.
主要成果:
- 提出的方法有效地估计LBA和DDM参数,处理相关的随机效应和共变量链接.
- 变量贝叶斯 (VB) 方法表现出显著的速度和效率,使得大规模估计问题和数据集的分析成为可能.
- 通过对三项现有实验研究的数据的应用来验证性能.
结论:
- 开发的方法提供了一种强大而灵活的方法,可以在层次化的贝叶斯框架内估计EAM.
- VB推理方法为大规模的认知建模应用提供了计算效率高的解决方案.
- 自由可用的代码和实现方便了这些复杂的建模技术的采用和发展.
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