Abstract—In this paper we demonstrate the inherent robustness of minimum distance estimator that makes it a potentially powerful tool for parameter estimation in gene expression ...
For many applied problems in the context of clustering via mixture models, the estimates of the component means and covariance matrices can be affected by observations that are at...
The K-Means and EM algorithms are popular in clustering and mixture modeling due to their simplicity and ease of implementation. However, they have several significant limitations...
In this paper, we propose a new nonparametric Bayesian framework to cluster white matter fiber tracts into bundles using a hierarchical Dirichlet processes mixture (HDPM) model. Th...
Carl-Fredrik Westin, W. Eric L. Grimson, Xiaogang ...
We propose mixtures of hidden Markov models for modelling clickstreams of web surfers. Hence, the page categorization is learned from the data without the need for a (possibly cumb...