Thu, 21 Aug 2008 14:01:37 BST CiteULike: mshafiei's dirichlet http://www.citeulike.org/user/mshafiei/tag/dirichlet CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/mshafiei/article/2546800 <i>Pattern Recogn. Lett., Vol. 26, No. 12. (September 2005), pp. 1916-1925.</i> Using unsupervised learning of a finite Dirichlet mixture model to improve pattern recognition applications Nizar Bouguila Djemel Ziou doi:10.1016/j.patrec.2005.03.016 Pattern Recogn. Lett., Vol. 26, No. 12. (September 2005), pp. 1916-1925. 2008-03-17T15:18:05-00:00 2005 Pattern Recogn. Lett. 0167-8655 26 12 1916 1925 Elsevier Science Inc. dirichlet mixture unsupervised http://www.citeulike.org/user/mshafiei/article/2500825 <i>Journal of the American Statistical Association, Vol. 64, No. 325. (1969), pp. 194-206.</i> Concepts of Independence for Proportions with a Generalization of the Dirichlet Distribution Robert Connor James Mosimann Journal of the American Statistical Association, Vol. 64, No. 325. (1969), pp. 194-206. 2008-03-10T13:55:18-00:00 1969 Journal of the American Statistical Association 64 325 194 206 dirichlet distribution independence proportions statistics http://www.citeulike.org/user/mshafiei/article/1239722 <i>(2005), pp. 297-304.</i> Bayesian hierarchical clustering Katherine Heller Zoubin Ghahramani doi:10.1145/1102351.1102389 (2005), pp. 297-304. 2007-04-20T13:27:16-00:00 2005 297 304 ACM Press bayesian clustering dirichlet hierarchical mixture models process http://www.citeulike.org/user/mshafiei/article/2437252 <i>International Statistical Review / Revue Internationale de Statistique, Vol. 67, No. 2. (1999), pp. 187-210.</i><br /><br />The object of this paper is to review the main results obtained in semi- and non-parametric Bayesian analysis of duration models. Standard nonparametric Bayesian models for independent and identically distributed observations are reviewed in line with Ferguson's pioneering papers. Recent results on the characterization of Dirichlet processes and on nonparametric treatment of censoring and of heterogeneity in the context of mixtures of Dirichlet processes are also discussed. The final section considers a Bayesian semiparametric version of the proportional hazards model. /// L'objectif de cet article est de présenter les résultats principaux obtenus dans l'analyse bayésienne semi-paramétrique ou non-paramétrique des modèles de durée. Les résultats fondamentaux du modèle de base (données indépendantes et identiquement distribuées) sont rappelés en suivant les travaux initiaux de Ferguson et en utilisant des résultats récents relatifs aux représentations du processus de Dirichlet. La considération de mélanges de processus de Dirichlet permet d'étudier l'impact de la présence de données censurées et l'introduction de l'hétérogénéité non observée. La dernière section examine le traitement bayésien de modèles semi-paramétriques à risques proportionnels. Semi- and Non-Parametric Bayesian Analysis of Duration Models with Dirichlet Priors: A Survey JP Florens M Mouchart JM Rolin International Statistical Review / Revue Internationale de Statistique, Vol. 67, No. 2. (1999), pp. 187-210. 2008-02-27T15:17:54-00:00 1999 International Statistical Review / Revue Internationale de Statistique 67 2 187 210 bayesian dirichlet duration-models nonparametric http://www.citeulike.org/user/mshafiei/article/634913 <i>Journal of Computational and Graphical Statistics, Vol. 9, No. 2. (2000), pp. 249-265.</i><br /><br />This article reviews Markov chain methods for sampling from the posterior distribution of a Dirichlet process mixture model and presents two new classes of methods. One new approach is to make Metropolis-Hastings updates of the indicators specifying which mixture component is associated with each observation, perhaps supplemented with a partial form of Gibbs sampling. The other new approach extends Gibbs sampling for these indicators by using a set of auxiliary parameters. These methods are simple to implement and are more efficient than previous ways of handling general Dirichlet process mixture models with non-conjugate priors. Markov Chain Sampling Methods for Dirichlet Process Mixture Models Radford Neal Journal of Computational and Graphical Statistics, Vol. 9, No. 2. (2000), pp. 249-265. 2006-05-15T02:10:22-00:00 2000 Journal of Computational and Graphical Statistics 9 2 249 265 bayesian dirichlet mcmc nonparametric http://www.citeulike.org/user/mshafiei/article/1903108 <i></i> Collapsed Variational Inference for HDP Yee Teh Kenichi Kurihara Max Welling 2007-11-12T15:31:33-00:00 dirichlet hierarchical inference variational http://www.citeulike.org/user/mshafiei/article/1952481 <i>(2004)</i> Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes YW Teh MI Jordan MJ Beal DM Blei (2004) 2007-11-21T15:18:40-00:00 2004 bayesian clustering dirichlet non-parametric http://www.citeulike.org/user/mshafiei/article/965794 <i>Journal of the American Statistical Association, Vol. 101, No. 476. (December 2006), pp. 1566-1581.</i> Hierarchical Dirichlet Processes Teh Yee Whye Jordan I Michael Beal J Matthew Blei M David doi:10.1198/016214506000000302 Journal of the American Statistical Association, Vol. 101, No. 476. (December 2006), pp. 1566-1581. 2006-11-29T01:37:01-00:00 2006 Journal of the American Statistical Association 0162-1459 101 476 1566 1581 American Statistical Association bayesian clustering dirichlet nonparametric