Search Machine Learning Repository: Lower Bounds for the Gibbs Sampler over Mixtures of Gaussians
Authors: Christopher Tosh and Sanjoy Dasgupta
Conference: Proceedings of the 31st International Conference on Machine Learning (ICML-14)
Year: 2014
Pages: 1467-1475
Abstract: The mixing time of a Markov chain is the minimum time $t$ necessary for the total variation distance between the distribution of the Markov chain's current state $X_t$ and its stationary distribution to fall below some $\epsilon > 0$. In this paper, we present lower bounds for the mixing time of the Gibbs sampler over Gaussian mixture models with Dirichlet priors.
[pdf] [BibTeX]

authors venues years
Suggest Changes to this paper.
Brought to you by the WUSTL Machine Learning Group. We have open faculty positions (tenured and tenure-track).