2009/05/02 by Ruth Fuentes–García, Ruth Fuentes-Garcia, Fuentes-Garcia, Ruth +6
Computer Science · Mathematics · #Algorithms and Data Compression #Applications (stat.AP) #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Stochastic processes and statistical mechanics #stat.AP
paper · pdf · doi:10.48550/arxiv.0905.0210
arxiv created 2009/05/02 · openalex publication_date 2009/05/02 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we provide an explicit probability distribution for classification purposes. It is derived from the Bayesian nonparametric mixture of Dirichlet process model, but with suitable modifications which remove unsuitable aspects of the classification based on this model. The resulting approach then more closely resembles a classical hierarchical grouping rule in that it depends on sums of squares of neighboring values. The proposed probability model for classification relies on a simulation algorithm which will be based on a reversible MCMC algorithm for determining the probabilities, and we provide numerical illustrations comparing with alternative ideas for classification.