Bounds on Lifting Continuous Markov Chains to Speed Up Mixing
Kavita Ramanan, Aaron Smith

TL;DR
This paper extends the evolving set method to derive bounds on the mixing times of lifted Markov chains on continuous state spaces, improving existing bounds and providing insights into speeding up mixing processes.
Contribution
It introduces a refined method for bounding mixing times of lifted Markov chains on continuous spaces, enhancing previous finite-space bounds and addressing continuous cases.
Findings
Derived bounds for continuous state space Markov chains.
Improved existing bounds for finite state space chains.
Provided theoretical tools for analyzing lifted Markov chains.
Abstract
It is often possible to speed up the mixing of a Markov chain on a state space by \textit{lifting}, that is, running a more efficient Markov chain on a larger state space that projects to in a certain sense. In [CLP99], Chen, Lov{\'a}sz and Pak prove that for Markov chains on finite state spaces, the mixing time of any lift of a Markov chain is at least the square root of the mixing time of the original chain, up to a factor that depends on the stationary measure. Unfortunately, this extra factor makes the bound in [CLP99] very loose for Markov chains on large state spaces and useless for Markov chains on continuous state spaces. In this paper, we develop an extension of the evolving set method that allows us to refine this extra factor and find…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMarkov Chains and Monte Carlo Methods · Bayesian Methods and Mixture Models · Machine Learning and Algorithms
