Dirichlet Process Gaussian Mixture Models： Choice of the Base Distribution

• Published in: Journal of computer science and technology Vol. 25; no. 4; pp. 653 - 664
• Main Authors: Görür, Dilan, Edward Rasmussen, Carl
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.07.2010; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Charitable foundations; Computational efficiency; Computer Science; Computer simulation; Conjugates; Data Structures and Information Theory; Density; Dirichlet; Dirichlet problem; DPM模型; Gaussian; Gaussian process; Information Systems Applications (incl.Internet); Markov analysis; Markov chains; Mathematical models; Mixtures; Monte Carlo methods; Probabilistic models; Regular Paper; Software Engineering; Theory of Computation; 元件模型; 模型框架; 混合元件; 组成部分; 马尔可夫链; 高斯混合模型; Dirichlet processes; Bayesian nonparametrics; Gaussian mixtures
• ISSN: 1000-9000; 1860-4749
• DOI: 10.1007/s11390-010-9355-8

In the Bayesian mixture modeling framework it is possible to infer the necessary number of components to model the data and therefore it is unnecessary to explicitly restrict the number of components. Nonparametric mixture models sidestep the problem of finding the ＂correct＂ number of mixture compon...