Repetition effects in a Sequential Monte Carlo sampler
Sarah Cannon, Daryl DeFord, Moon Duchin
TL;DR
This paper examines how often samples are repeated in a Sequential Monte Carlo method applied to political redistricting, highlighting the impact of repetition on the sampling process.
Contribution
It provides an analysis of sample repetition effects in a specific SMC algorithm used for redistricting, a topic not extensively studied before.
Findings
Sample repetition occurs frequently in the SMC method.
Repetition influences the efficiency and accuracy of the sampling process.
Insights into managing repetition can improve SMC performance.
Abstract
We investigate the prevalence of sample repetition in a Sequential Monte Carlo (SMC) method recently introduced for political redistricting.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsForecasting Techniques and Applications · Statistical Methods and Inference