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)
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.
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).