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• W2550751211 abstract "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 a corollary, this result yields a fast mixing sampler for Determinantal Point Processes (DPPs), yielding (to our knowledge) the first provably fast MCMC sampler for DPPs since their inception over four decades ago. Beyond SR measures, we develop MCMC samplers for probabilistic models with hard constraints and identify sufficient conditions under which their chains mix rapidly. We illustrate our claims by empirically verifying the dependence of mixing times on the key factors governing our theoretical bounds." @default.
• W2550751211 created "2016-11-30" @default.
• W2550751211 creator A5044707901 @default.
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• W2550751211 date "2016-08-02" @default.
• W2550751211 modified "2023-10-17" @default.
• W2550751211 title "Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling" @default.
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• W2550751211 doi "https://doi.org/10.48550/arxiv.1608.01008" @default.
• W2550751211 hasPublicationYear "2016" @default.
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