Variational Inference for Dirichlet Process Mixtures Export

@misc{citeulike:5442289, abstract = {Dirichlet process (DP) mixture models are the cornerstone of nonparametric Bayesian statistics, and the development of Monte-Carlo Markov chain (MCMC) sampling methods for DP mixtures has enabled the application of nonparametric Bayesian methods to a variety of practical data analysis problems.}, author = {Blei, David M. and Jordan, Michael I.}, citeulike-article-id = {5442289}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.61.4467}, keywords = {bayesian, dirichlet-process, mixture, nonparametric, variational-inference}, posted-at = {2009-09-25 13:45:28}, priority = {2}, title = {Variational Inference for Dirichlet Process Mixtures}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.61.4467}, year = {2006} }

TY - ELEC ID - citeulike:5442289 L3 - citeulike-article-id:5442289 N2 - Dirichlet process (DP) mixture models are the cornerstone of nonparametric Bayesian statistics, and the development of Monte-Carlo Markov chain (MCMC) sampling methods for DP mixtures has enabled the application of nonparametric Bayesian methods to a variety of practical data analysis problems. TI - Variational Inference for Dirichlet Process Mixtures KW - bayesian KW - dirichlet-process KW - mixture KW - nonparametric KW - variational-inference AU - Blei, David AU - Jordan, Michael PY - 2006/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.61.4467 ER -