Introduction to 'On the use of Markov chain Monte Carlo methods for the sampling of mixture models' by R. Douc, F. Maire, J. Olsson

Stefano Peluso

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Introduction to 'On the use of Markov chain Monte Carlo methods for the sampling of mixture models' by R. Douc, F. Maire, J. Olsson

机译：R. Douc，F.Maire，J.Olsson的“关于使用马尔可夫链蒙特卡罗方法进行混合模型采样”简介

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The Authors compare, theoretically and by simulations, various algorithms for sampling mixture models. The starting point is the algorithm of Carlin and Chib (1995), which augments the parameter space through the involvement of so-called pseudo priors or linking functions. Carlin and Chib (1995) originally introduced their algorithm (from now on, the CC algorithm) with the purpose of overcoming some limitations implicit in more standard MCMC approaches, for instance in the case of models with parameter space of varying size. In the development of the present paper, the CC algorithm is implemented in the context of mixture models of fixed size, with the aim of improving the mixing of a standard Gibbs sampler, whose performance is negatively affected by the high correlation between mixture weights and components.

机译：作者从理论上和通过仿真比较了各种用于采样混合模型的算法。起点是Carlin和Chib（1995）的算法，该算法通过涉及所谓的伪先验或链接函数来扩大参数空间。 Carlin和Chib（1995）最初引入他们的算法（从现在开始是CC算法）的目的是克服更标准的MCMC方法所隐含的一些限制，例如在参数空间大小可变的模型中。在本文的开发中，CC算法是在固定大小的混合模型的上下文中实现的，目的是改善标准Gibbs采样器的混合，该采样器的性能受到混合权重和组分之间的高度相关性的负面影响。

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《Statistics and computing》 |2015年第1期|93-94|共2页
作者
Stefano Peluso;
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Swiss Finance Institute, University of Lugano, Via Giuseppe Buffi 13, 6904 Lugano, Switzerland;

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Introduction to 'On the use of Markov chain Monte Carlo methods for the sampling of mixture models' by R. Douc, F. Maire, J. Olsson

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