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2014 ICML ICML 2014

Lower Bounds for the Gibbs Sampler over Mixtures of Gaussians

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 ε> 0. In this paper, we present lower bounds for the mixing time of the Gibbs sampler over Gaussian mixture models with Dirichlet priors.

🌉 Interdisciplinary Bridge — Machine Learning and Mathematics & Optimization
🐣 Hot Topic Early Bird — markov chain monte carlo
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Christopher Tosh , Sanjoy Dasgupta

Topics

Machine Learning > Optimization & Theory > Bayesian Inference Mathematics & Optimization > Mathematics > Probability Mathematics & Optimization > Optimization > Stochastic Methods Machine Learning > Bayesian & Probabilistic > Bayesian Inference Mathematics & Optimization > Probability > Stochastic Processes

Keywords

bayesian inference markov chain monte carlo gibbs sampling markov chain stochastic process gibbs sampler mixing time gaussian mixture model mixture model dirichlet prior
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