Online Program Home
  My Program

Abstract Details

Activity Number: 24 - Statistical Computing and Graphics Student Awards
Type: Topic Contributed
Date/Time: Sunday, July 30, 2017 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract #323463
Title: The Self-Multiset Sampler and Its Generalization
Author(s): Weihong Huang* and Juan Shen and Yuguo Chen
Companies: University of Illinois at Urbana-Champaign and Fudan University and University of Illinois at Urbana-Champaign
Keywords: Metropolis-Hastings algorithm ; Multiset ; Data augmentation ; Multimodal

The Metropolis-Hastings algorithm is one of the most well-known Markov chain Monte Carlo method. However, the M-H algorithm can easily get trapped in a local mode because of the stickiness of the samples. The multiset sampler has been shown to be an effective algorithm to sample from complex multimodal distributions, but the multiset sampler requires that the parameters in the target distribution can be divided into two parts: the parameters of interest and the nuisance parameters. We propose a new self-multiset sampler (SMSS) which extends the multiset sampler to distributions without nuisance parameters. We also generalize our method to distributions with unbounded or infinite support. Numerical results show that the SMSS and its generalization have a substantial advantage in sampling multimodal distributions compared to the ordinary Markov chain Monte Carlo algorithm and some popular variants. Supplemental materials for the article are available online.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2017 program

Copyright © American Statistical Association