Operations research: an introduction, 4th ed.
Operations research: an introduction, 4th ed.
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Editorial: recent developments in mixture models
Computational Statistics & Data Analysis
Bayesian analysis of finite mixture models of distributions from exponential families
Computational Statistics
Editorial: Advances in Mixture Models
Computational Statistics & Data Analysis
Complexity control in a mixture model by the Hardy-Weinberg equilibrium
Computational Statistics & Data Analysis
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The Bayesian implementation of finite mixtures of distributions has been an area of considerable interest within the literature. Computational advances on approximation techniques such as Markov chain Monte Carlo (MCMC) methods have been a keystone to Bayesian analysis of mixture models. This paper deals with the Bayesian analysis of finite mixtures of two particular types of multidimensional distributions: the multinomial and the negative-multinomial ones. A unified framework addressing the main topics in a Bayesian analysis is developed for the case with a known number of component distributions. In particular, theoretical results and algorithms to solve the label-switching problem are provided. An illustrative example is presented to show that the proposed techniques are easily applied in practice.