2015
Adaptive Low-Complexity Sequential Inference for Dirichlet Process Mixture Models
NeurIPS 2015poster
We develop a sequential low-complexity inference procedure for Dirichlet process mixtures of Gaussians for online clustering and parameter estimation when the number of clusters are unknown a-priori. We present an easily computable, closed form parametric expression for the conditional likelihood, i…