Convergence of Gibbs Sampling: Coordinate Hit-and-Run Mixes Fast

The Gibbs Sampler is a general method for sampling high-dimensional distributions, dating back to Turchin, 1971. In each step of the Gibbs Sampler, we pick a random coordinate and re-sample that coordinate from the distribution induced by fixing all other coordinates. While it has become widely used over the past half-century, guarantees of efficient convergence have been elusive. We show that for a convex body $K$ in $\mathbb{R}^{n}$ with diameter $D$, the mixing time of the Coordinate Hit-and-Run (CHAR) algorithm on $K$ is polynomial in $n$ and $D$. We also give a lower bound on the conductance of CHAR, showing that it is strictly worse than hit-and-run or the ball walk in the worst case.

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