贝叶斯定理
计算机科学
贝叶斯概率
统计推断
多级模型
统计模型
机器学习
推论
分层数据库模型
贝叶斯推理
计量经济学
贝叶斯因子
人工智能
贝叶斯统计
贝叶斯分层建模
数据挖掘
统计
数学
作者
Udo Boehm,Nathan J. Evans,Quentin Frederik Gronau,Dora Matzke,Eric‐Jan Wagenmakers,Andrew J. Heathcote
摘要
Cognitive models provide a substantively meaningful quantitative description of latent cognitive processes. The quantitative formulation of these models supports cumulative theory building and enables strong empirical tests. However, the nonlinearity of these models and pervasive correlations among model parameters pose special challenges when applying cognitive models to data. Firstly, estimating cognitive models typically requires large hierarchical data sets that need to be accommodated by an appropriate statistical structure within the model. Secondly, statistical inference needs to appropriately account for model uncertainty to avoid overconfidence and biased parameter estimates. In the present work, we show how these challenges can be addressed through a combination of Bayesian hierarchical modeling and Bayesian model averaging. To illustrate these techniques, we apply the popular diffusion decision model to data from a collaborative selective influence study. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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