比较基于概率和无概率的拟合方法,并比较时间间选择模型
Peter D Kvam1, Konstantina Sokratous2, Anderson K Fitch2
1The Ohio State University, 1835 Neil Ave, Columbus, OH, 43210, USA. kvam.4@osu.edu.
Behavior research methods
|August 12, 2025
概括
神经网络和贝叶斯方法在物质使用研究的认知建模中显示出一致. 然而,神经网络在模型比较和参数估计方面表现出色,特别是在大型数据集中.
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.
- 机器学习 机器学习
背景情况:
- 机器学习 (ML) 越来越多地用于认知模型的拟合和比较,特别是对于缺乏可处理的概率的模型.
- 将ML方法与传统的基于概率的方法进行比较,对于理解ML在开发新的认知模型和理论中的实用性至关重要.
研究的目的:
- 系统地比较神经网络 (NN) 方法与基于概率的认知模型拟合和比较方法.
- 将这些方法应用于使用物质使用问题的个人数据的时间间选择建模.
主要方法:
- 与传统的基于概率的方法对比NN方法的基准评估.
- 将两种方法应用于时间间选择数据.
- 探索NN方法的扩展,使用循环和脱落层来适应复杂的数据和后续采样.
主要成果:
- 在推断潜在过程和物质使用结果方面,NN和贝叶斯方法表明了趋同.
- 在模型比较中,分类网络的表现明显优于基于概率的指标.
- NNs适用于快速参数估计,后端采样,大数据集和模型比较.
结论:
- 对于特定的建模任务,如参数估计和比较,NN提供了优势,特别是在大型数据集.
- 贝叶斯马尔科夫链蒙特卡洛 (MCMC) 方法对于具有复杂实验设计的较小数据集仍然是可取的.
- 这项研究强调了机器学习和传统方法在推进认知建模方面的互补作用.
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