A Survey of Model Evaluation Approaches With a Tutorial on Hierarchical Bayesian Methods
A Survey of Model Evaluation Approaches With a Tutorial on Hierarchical Bayesian Methods
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DOI：
10.1080/03640210802414826
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发表时间：
2008-01-01
影响因子：
2.5
通讯作者：
Wagenmakers, Eric-Jan
中科院分区：
文献类型：
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作者：
Shiffrin, Richard M.;Lee, Michael D.;Wagenmakers, Eric-Jan
本文回顾了当前认知科学中模型评估的方法，包括基于理论的方法，如贝叶斯因子和最小描述长度测量;模拟方法，包括模型模仿评估;和实用方法，如验证和推广措施。本文认为，虽然在特定环境中经常有用，但大多数这些方法在对模型进行一般评估的能力方面是有限的。本文认为，分层方法，一般来说，分层贝叶斯方法，具体来说，可以提供一个更全面的评估模型在认知科学。本文介绍了两个工作的分层贝叶斯分析的例子，以展示如何解决的描述充分性，参数干扰，预测和概括的原则和连贯的方式的关键问题的方法。
This article reviews current methods for evaluating models in the cognitive sciences, including theoretically based approaches, such as Bayes factors and minimum description length measures; simulation approaches, including model mimicry evaluations; and practical approaches, such as validation and generalization measures. This article argues that, although often useful in specific settings, most of these approaches are limited in their ability to give a general assessment of models. This article argues that hierarchical methods, generally, and hierarchical Bayesian methods, specifically, can provide a more thorough evaluation of models in the cognitive sciences. This article presents two worked examples of hierarchical Bayesian analyses to demonstrate how the approach addresses key questions of descriptive adequacy, parameter interference, prediction, and generalization in principled and coherent ways.