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

被引：7
|
作者：
Griffin, J. E. [1 ]
机构：
[1] Univ Kent, Sch Math Stat & Actuarial Sci, Canterbury, Kent, England
来源：
STATISTICS AND COMPUTING | 2017年 / 27卷 / 01期
关键词：
Bayesian nonparametrics; Dirichlet process; Normalized generalized gamma process; Nonparametric stochastic volatility; Slice sampling; Particle Gibbs sampling; BAYESIAN-INFERENCE; DENSITY-ESTIMATION; SAMPLING METHODS; PARTICLE FILTER; MODEL; DISTRIBUTIONS; VOLATILITY; GIBBS;
D O I：
10.1007/s11222-015-9612-3
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
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：15
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