Circular data are encountered in a variety of fields. A dataset on music listening behaviour throughout the day motivates development of models for multi-modal circular data where the number of modes is not known a priori. To fit a mixture model with an unknown number of modes, the reversible jump Metropolis-Hastings MCMC algorithm is adapted for circular data and presented. The performance of this sampler is investigated in a simulation study. At small-to-medium sample sizes , the number of components is uncertain. At larger sample sizes the estimation of the number of components is accurate. Application to the music listening data shows interpretable results that correspond with intuition.

中文翻译：

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使用可逆跳跃 MCMC 采样器对 von Mises 分布的混合进行贝叶斯推理

在各种领域都会遇到循环数据。全天音乐收听行为的数据集激发了多模态循环数据模型的开发，其中模式的数量是先验未知的。为了拟合具有未知数量模式的混合模型，可逆跳跃 Metropolis-Hastings MCMC 算法适用于循环数据并呈现。在模拟研究中研究了该采样器的性能。在中小样本量中，成分的数量是不确定的。在较大的样本量下，组件数量的估计是准确的。对音乐收听数据的应用显示了与直觉相对应的可解释结果。