Sequential Monte Carlo methods for mixtures with normalized random measures with independent increments priors

被引：0
作者：
J. E. Griffin
机构：
[1] University of Kent,School of Mathematics, Statistics and Actuarial Science
来源：
Statistics and Computing | 2017年 / 27卷
关键词：
Bayesian nonparametrics; Dirichlet process; Normalized generalized gamma process; Nonparametric stochastic volatility; Slice sampling; Particle Gibbs sampling;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Normalized random measures with independent increments are a general, tractable class of nonparametric prior. This paper describes sequential Monte Carlo methods for both conjugate and non-conjugate nonparametric mixture models with these priors. A simulation study is used to compare the efficiency of the different algorithms for density estimation and comparisons made with Markov chain Monte Carlo methods. The SMC methods are further illustrated by applications to dynamically fitting a nonparametric stochastic volatility model and to estimation of the marginal likelihood in a goodness-of-fit testing example.
引用
收藏
页码：131 / 145
页数：14
相关论文
共 89 条
[1]  
Andrieu C(2010)Particle Markov chain Monte Carlo methods (with discussion) J. R. Stat. Soc. Ser. B 72 269-342
[2]  
Doucet A(2014)Beta-product dependent Pitman–Yor processes for Bayesian inference J. Econom. 180 49-72
[3]  
Holenstein R(2003)Marginal likelihood and Bayes factors for Dirichlet process mixture models J. Am. Stat. Assoc. 98 224-235
[4]  
Bassetti F(2001)Testing of a parametric model versus nonparametric alternatives J. Am. Stat. Assoc. 96 174-184
[5]  
Casarin R(1973)Ferguson distributions via Polya Urn schemes Ann. Stat. 1 353-355
[6]  
Leisen F(1999)Generalized gamma measures and shot-noise Cox processes Adv. Appl. Probab. 31 929-953
[7]  
Basu S(2008)Bayesian inference for linear dynamics models with Dirichlet process mixtures IEEE Trans. Signal Process. 56 71-84
[8]  
Chib S(1999)An improved particle filter for non-linear problems IEE Proc. Radar Sonar Navig. 146 2-7
[9]  
Berger J(2010)Particle learning for general mixtures Bayesian Anal. 5 709-740
[10]  
Guglielmi A(2002)A sequential particle filter for static models Biometrika 89 539-551
123456789