标题： 排斥混合物 显示英文标题

标题： Repulsive Mixtures

摘要： 离散混合模型通常用于密度估计和聚类。在对特定簇的参数进行推断时，当前的频率学方法和贝叶斯方法常常遇到问题，当簇过于接近而无法在科学上具有意义时尤其如此。目前的贝叶斯实践生成独立于公共先验的组件特定参数，这往往倾向于支持相似的组件，并经常导致分配大量概率给那些为了拟合数据而不需要的冗余组件。作为替代方案，我们建议从排斥过程生成组件，这将导致较少、分离更好且更易解释的簇。我们从理论上描述了这种排斥先验，并提出了一个马尔可夫链蒙特卡洛采样算法来进行后验计算。这些方法通过模拟数据以及真实数据集进行了说明。 显示英文摘要

摘要： Discrete mixture models are routinely used for density estimation and clustering. While conducting inferences on the cluster-specific parameters, current frequentist and Bayesian methods often encounter problems when clusters are placed too close together to be scientifically meaningful. Current Bayesian practice generates component-specific parameters independently from a common prior, which tends to favor similar components and often leads to substantial probability assigned to redundant components that are not needed to fit the data. As an alternative, we propose to generate components from a repulsive process, which leads to fewer, better separated and more interpretable clusters. We characterize this repulsive prior theoretically and propose a Markov chain Monte Carlo sampling algorithm for posterior computation. The methods are illustrated using simulated data as well as real datasets.