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

Authors Aditi Laddha , Santosh S. Vempala

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Author Details

Aditi Laddha
  • Georgia Institute of Technology, Atlanta, GA, USA
Santosh S. Vempala
  • Georgia Institute of Technology, Atlanta, GA, USA

Funding

  • Laddha, Aditi: Supported in part by NSF awards 1839323 and 2007443.
  • Vempala, Santosh S.: Supported in part by NSF awards 1839323, 1909756 and 2007443.

Cite AsGet BibTex

Aditi Laddha and Santosh S. Vempala. Convergence of Gibbs Sampling: Coordinate Hit-And-Run Mixes Fast. In 37th International Symposium on Computational Geometry (SoCG 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 189, pp. 51:1-51:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/LIPIcs.SoCG.2021.51

Abstract

The Gibbs Sampler is a general method for sampling high-dimensional distributions, dating back to 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 the 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 ℝⁿ 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 mixing rate of CHAR, showing that it is strictly worse than hit-and-run and the ball walk in the worst case.

Subject Classification

ACM Subject Classification
  • Mathematics of computing → Markov processes
Keywords
  • Gibbs Sampler
  • Coordinate Hit and run
  • Mixing time of Markov Chain

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