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.

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。