Nonparametric Predictive Inference for Discrete Data via Metropolis-Adjusted Dirichlet Sequences
基于 Metropolis 调整狄利克雷序列的离散数据非参数预测推断
This article is motivated by challenges in conducting Bayesian inferences on unknown discrete distributions, with a particular focus on count data. To avoid the computational disadvantages of traditional mixture models, we develop a novel Bayesian predictive approach. In particular, our Metropolis-adjusted Dirichlet (mad) sequence model characterizes the predictive measure as a mixture of a base measure and Metropolis-Hastings kernels centered on previous data points. The…