A practical sampling approach for a Bayesian mixture model with unknown number of components Export

@article{citeulike:1604300, abstract = {Abstract\ \ Recently, mixture distribution becomes more and more popular in many scientific fields. Statistical computation and analysis of mixture models, however, are extremely complex due to the large number of parameters involved. Both EM algorithms for likelihood inference and MCMC procedures for Bayesian analysis have various difficulties in dealing with mixtures with unknown number of components. In this paper, we propose a direct sampling approach to the computation of Bayesian finite mixture models with varying number of components. This approach requires only the knowledge of the density function up to a multiplicative constant. It is easy to implement, numerically efficient and very practical in real applications. A simulation study shows that it performs quite satisfactorily on relatively high dimensional distributions. A well-known genetic data set is used to demonstrate the simplicity of this method and its power for the computation of high dimensional Bayesian mixture models.}, author = {Wang, Liqun and Fu, James}, citeulike-article-id = {1604300}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/s00362-007-0361-4}, day = {28}, doi = {10.1007/s00362-007-0361-4}, journal = {Statistical Papers}, keywords = {bayes, gibbs, hierarchical, nonparametric}, month = {October}, number = {4}, pages = {631--653}, posted-at = {2008-07-03 22:26:48}, priority = {2}, title = {A practical sampling approach for a Bayesian mixture model with unknown number of components}, url = {http://dx.doi.org/10.1007/s00362-007-0361-4}, volume = {48}, year = {2007} }

TY - JOUR ID - citeulike:1604300 L3 - citeulike-article-id:1604300 N2 - Abstract  Recently, mixture distribution becomes more and more popular in many scientific fields. Statistical computation and analysis of mixture models, however, are extremely complex due to the large number of parameters involved. Both EM algorithms for likelihood inference and MCMC procedures for Bayesian analysis have various difficulties in dealing with mixtures with unknown number of components. In this paper, we propose a direct sampling approach to the computation of Bayesian finite mixture models with varying number of components. This approach requires only the knowledge of the density function up to a multiplicative constant. It is easy to implement, numerically efficient and very practical in real applications. A simulation study shows that it performs quite satisfactorily on relatively high dimensional distributions. A well-known genetic data set is used to demonstrate the simplicity of this method and its power for the computation of high dimensional Bayesian mixture models. IS - 4 JF - Statistical Papers EP - 653 TI - A practical sampling approach for a Bayesian mixture model with unknown number of components VL - 48 SP - 631 KW - bayes KW - gibbs KW - hierarchical KW - nonparametric AU - Wang, Liqun AU - Fu, James PY - 2007/10/28/ UR - http://dx.doi.org/10.1007/s00362-007-0361-4 ER -