# A Polynomial Time MCMC Method for Sampling from Continuous DPPs

arXiv: https://arxiv.org/abs/1810.08867 · HTML: https://mlsanity.com/paper/1810.08867/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2018.

## Built on by (most cited first, 1 of 1)

- [Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence](https://mlsanity.com/paper/2204.02570/) (2022)

## Abstract (arXiv)

We study the Gibbs sampling algorithm for continuous determinantal point processes. We show that, given a warm start, the Gibbs sampler generates a random sample from a continuous k-DPP defined on a d-dimensional domain by only taking poly(k) number of steps. As an application, we design an algorithm to generate random samples from k-DPPs defined by a spherical Gaussian kernel on a unit sphere in d-dimensions, mathbbSd-1 in time polynomial in k,d.