The infinite Gaussian mixture model Export

@inproceedings{citeulike:3478609, abstract = {In a Bayesian mixture model it is not necessary a priori to limit the number of components to be finite. In this paper an infinite Gaussian mixture model is presented which neatly sidesteps the difficult problem of finding the \"right \" number of mixture components. Inference in the model is done using an efficient parameter-free Markov Chain that relies entirely on Gibbs sampling. 1}, author = {Rasmussen, Carl E.}, booktitle = {In Advances in Neural Information Processing Systems 12}, citeulike-article-id = {3478609}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.45.9111}, keywords = {infinite\_mixturemodels}, pages = {554--560}, posted-at = {2008-11-04 14:27:00}, priority = {2}, title = {The infinite Gaussian mixture model}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.45.9111}, volume = {12}, year = {2000} }

TY - CONF ID - citeulike:3478609 L3 - citeulike-article-id:3478609 N2 - In a Bayesian mixture model it is not necessary a priori to limit the number of components to be finite. In this paper an infinite Gaussian mixture model is presented which neatly sidesteps the difficult problem of finding the "right " number of mixture components. Inference in the model is done using an efficient parameter-free Markov Chain that relies entirely on Gibbs sampling. 1 EP - 560 TI - The infinite Gaussian mixture model VL - 12 T2 - In Advances in Neural Information Processing Systems 12 SP - 554 KW - infinite_mixturemodels AU - Rasmussen, Carl PY - 2000/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.45.9111 ER -