# Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling

@inproceedings{Li2016FastMM, title={Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling}, author={Chengtao Li and Suvrit Sra and Stefanie Jegelka}, booktitle={NIPS}, year={2016} }

We study probability measures induced by set functions with constraints. Such measures arise in a variety of real-world settings, where prior knowledge, resource limitations, or other pragmatic considerations impose constraints. We consider the task of rapidly sampling from such constrained measures, and develop fast Markov chain samplers for them. Our first main result is for MCMC sampling from Strongly Rayleigh (SR) measures, for which we present sharp polynomial bounds on the mixing time. As…

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