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