在贝叶斯物品响应理论中比较pystan和numpyro:评估估计的潜在参数和采样性能一致性的评估
Mizuho Nishio1, Eiji Ota2, Hidetoshi Matsuo1
1Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan.
PeerJ. Computer science
|October 23, 2023
概括
这项研究将PyStan和NumPyro与贝叶斯物品响应理论 (IRT) 模型进行比较. 两种库在参数估计方面都表现出很好的一致性,NumPyro经常提供更快的采样,特别是对于大型数据集的GPU加速.
科学领域:
- 计算统计的计算统计.
- 心理测量 心理测量 心理测量
- 贝叶斯的推理 贝叶斯的推理
背景情况:
- 项目响应理论 (IRT) 模型在教育和心理评估中被广泛使用.
- 对IRT的贝叶斯式方法在建模复杂关系和整合先前知识方面提供了灵活性.
- 马尔科夫链蒙特卡洛 (MCMC) 方法对于适应贝叶斯模型至关重要,但计算性能可能是一个瓶.
研究的目的:
- 为了比较两个流行的MCMC图书馆,PyStan和NumPyro,贝叶斯物件响应理论 (IRT) 模型的性能.
- 为了评估PyStan和NumPyro之间估计的潜在参数的协议.
- 在IRT的背景下,评估两个图书馆的计算效率 (采样时间).
主要方法:
- 实施贝叶斯的1参数 (1PL-IRT) 和2参数 (2PL-IRT) 模型,使用PyStan和NumPyro.
- 将实施的模型应用于已发表文章中的两种类型的医疗数据.
- 使用相同的先前分布,比较隐性参数和计算成本 (采样时间) 的估计结果.
主要成果:
- 在所有IRT类型和数据中,PyStan和NumPyro之间估计的潜在参数的平均值和标准偏差中观察到很好的一致性.
- 与PyStan相比,NumPyro对MCMC的采样时间通常较短.
- 在使用图形处理单元 (GPU) 处理大型模拟数据时,NumPyro显示了显著的速度改进.
结论:
- 无论是PyStan还是NumPyro都是有效的库,可以实现贝叶斯式1PL-IRT和2PL-IRT模型.
- 图书馆为潜在参数提供了可比的估计准确性.
- 选择最快的库用于MCMC采样取决于数据集大小,NumPyro对较大的数据集显示了优势,特别是在支持GPU的情况下.
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