Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models

被引：0

作者：

Bouchard-Cote, Alexandre ^{[1
]}

Doucet, Arnaud ^{[2
]}

Roth, Andrew ^{[2
,3
]}

机构：

[1] Univ British Columbia, Dept Stat, 3182 Earth Sci Bldg,2207 Main Mall, Vancouver, BC V6T 1Z4, Canada

[2] Univ Oxford, Dept Stat, Oxford, England

[3] Univ Oxford, Ludwig Inst Canc Res, Oxford, England

来源：

JOURNAL OF MACHINE LEARNING RESEARCH | 2017年 / 18卷

基金：

加拿大自然科学与工程研究理事会; 英国工程与自然科学研究理事会;

关键词：

Dirichlet process mixture models; Gibbs sampler; Particle Gibbs sampler; Sequential Monte Carlo; SEQUENTIAL MONTE-CARLO; UNKNOWN NUMBER;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents an original Markov chain Monte Carlo method to sample from the posterior distribution of conjugate mixture models. This algorithm relies on a flexible split-merge procedure built using the particle Gibbs sampler introduced in Andrieu et al. (2009, 2010). The resulting so-called Particle Gibbs Split-Merge sampler does not require the computation of a complex acceptance ratio and can be implemented using existing sequential Monte Carlo libraries. We investigate its performance experimentally on synthetic problems as well as on geolocation data. Our results show that for a given computational budget, the Particle Gibbs Split-Merge sampler empirically outperforms existing split merge methods. The code and instructions allowing to reproduce the experiments is available at https://github.com/aroth85/pgsm

引用

页数：39