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Reflections on Murray Aitkin's contributions to nonparametric mixture models and Bayes factors
Statistical Modelling ( IF 1.2 ) Pub Date : 2021-02-08 , DOI: 10.1177/1471082x20981312
Alan Agresti 1 , Francesco Bartolucci 2 , Antonietta Mira 3, 4
Affiliation  

We describe two interesting and innovative strands of Murray Aitkin's research publications, dealing with mixture models and with Bayesian inference. Of his considerable publications on mixture models, we focus on a nonparametric random effects approach in generalized linear mixed modelling, which has proven useful in a wide variety of applications. As an early proponent of ways of implementing the Bayesian paradigm, Aitkin proposed an alternative Bayes factor based on a posterior mean likelihood. We discuss these innovative approaches and some research lines motivated by them and also suggest future related methodological implementations.



中文翻译:

关于默里·艾特金(Murray Aitkin)对非参数混合模型和贝叶斯因子的贡献的思考

我们描述了穆雷·艾特金(Murray Aitkin)研究出版物的两个有趣且创新的方面,涉及混合模型和贝叶斯推理。在他关于混合模型的大量出版物中,我们专注于广义线性混合建模中的非参数随机效应方法,该方法已被证明可用于多种应用。作为实现贝叶斯范式的早期支持者,艾特金(Aitkin)根据后验平均似然率提出了另一种贝叶斯因子。我们讨论了这些创新方法以及由它们激发的一些研究方向,并提出了未来相关的方法学实施方案。

更新日期:2021-02-09
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