Sequential Bayesian inference for static parameters in dynamic state space models

被引：5

作者：

Bhattacharya, Arnab ^{[1
]}

Wilson, Simon P. ^{[1
]}

机构：

[1] Trinity Coll Dublin, Sch Comp Sci & Stat, Dept Stat, Dublin 02, Ireland

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2018年 / 127卷

基金：

爱尔兰科学基金会;

关键词：

Sequential estimation; Static parameter; Dynamic state space models; Bayesian inference; Grid-based methods; PARTICLE FILTERS; LIKELIHOOD; ALGORITHM;

D O I：

10.1016/j.csda.2018.05.018

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

A method for sequential Bayesian inference of the static parameters of a dynamic state space model is proposed. The method is able to use any valid approximation to the filtering and prediction densities of the state process. It computes the posterior distribution of the static parameters on a discrete grid that tracks the support dynamically. For inference of the state process, the Kalman filter and its extensions as well as cubature filtering have been used. It is illustrated with several examples including the stochastic volatility model and the challenging Kitagawa model and is compared to both online and offline methods. It is shown to provide a good trade off between speed and performance. (C) 2018 Elsevier B.V. All rights reserved.

引用

页码：187 / 203

页数：17

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