The Self-Multiset Sampler
Weihong Huang, Juan Shen, Yuguo Chen · Journal of Computational and Graphical Statistics · 2017
Weihong Huanga, Juan Shenb & Yuguo Chena*a Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, ILb Department of Statistics, Fudan University, Shanghai, ChinaCONTACT Yuguo Chen [email protected] Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL 61820Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/r/JCGS.Supplementary materials for this article are available online. Please go to www.tandfonline.com/r/JCGS.ABSTRACTThe 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.