Fast Sampling for Strongly Rayleigh Measures with Application to Determinantal Point Processes

TL;DR

This paper introduces a fast sampling method for strongly Rayleigh measures, enabling efficient sampling from Determinantal Point Processes, which are useful in diverse applications like machine learning and statistical modeling.

Contribution

The paper presents a novel, efficient Markov Chain sampler for strongly Rayleigh measures, significantly improving sampling speed for Determinantal Point Processes.

Findings

Developed a fast mixing Markov Chain sampler

Achieved efficient sampling for DPPs

Enhanced practical applicability of Rayleigh measures

Abstract

In this note we consider sampling from (non-homogeneous) strongly Rayleigh probability measures. As an important corollary, we obtain a fast mixing Markov Chain sampler for Determinantal Point Processes.