Hierarchical Approximate Bayesian Computation

被引:0
作者
Brandon M. Turner
Trisha Van Zandt
机构
[1] Stanford University,
[2] The Ohio State University,undefined
来源
Psychometrika | 2014年 / 79卷
关键词
approximate Bayesian computation; hierarchical Bayesian estimation; signal detection theory; dynamic signal detection;
D O I
暂无
中图分类号
学科分类号
摘要
Approximate Bayesian computation (ABC) is a powerful technique for estimating the posterior distribution of a model’s parameters. It is especially important when the model to be fit has no explicit likelihood function, which happens for computational (or simulation-based) models such as those that are popular in cognitive neuroscience and other areas in psychology. However, ABC is usually applied only to models with few parameters. Extending ABC to hierarchical models has been difficult because high-dimensional hierarchical models add computational complexity that conventional ABC cannot accommodate. In this paper, we summarize some current approaches for performing hierarchical ABC and introduce a new algorithm called Gibbs ABC. This new algorithm incorporates well-known Bayesian techniques to improve the accuracy and efficiency of the ABC approach for estimation of hierarchical models. We then use the Gibbs ABC algorithm to estimate the parameters of two models of signal detection, one with and one without a tractable likelihood function.
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页码:185 / 209
页数:24
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