SILO: Markov Chains beyond Rapid Mixing
Abstract Markov chain-based methods are ubiquitous and have been highly successful at statistical inference, scientific simulation, and optimization. The common wisdom is that the reason for their success is that a Markov chain of interest “mixes” to its stationary distribution. But what if the Markov chain does not mix fast? …