  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.
