Pseudo-Marginal MCMC for Parameter Estimation in alpha-Stable Distributions

被引：3

作者：

Riabiz, Marina ^{[1
]}

Lindsten, Fredrik ^{[1
]}

Godsill, Simon ^{[1
]}

机构：

[1] Univ Cambridge, Dept Engn, Signal Proc & Commun Lab, Cambridge CB2 1PZ, England

来源：

IFAC PAPERSONLINE | 2015年 / 48卷 / 28期

基金：

英国工程与自然科学研究理事会;

关键词：

BAYESIAN-INFERENCE;

D O I：

10.1016/j.ifacol.2015.12.173

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The alpha-stable distribution is very useful for modelling data with extrelne values and skewed behaviour. The distribution is governed by two key parameters, tail thickness and skewness, in addition to scale and location. Inferring these parameters is difficult due to the lack of a closed form expression of the probability density. We develop a Bayesian method, based on the pseudo-marginal MCMC approach, that requires only unbiased estimates of the intractable likelihood. To compute these estimates we build an adaptive importance sampler for a latent variable-representation of the alpha-stable density. This representation has previously been used in the literature for conditional MCMC sampling of the parameters, and we compare our method with this approach. (C) 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：472 / 477

页数：6