Open AccessVariational Bayesian inference of linear state space models
Author(s) -
Pan Chuanchao,
Wang Jingzhuo,
Dong Zijian
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.9048
Subject(s) - state space representation , bayesian inference , bayesian probability , state space , gaussian , linear model , algorithm , mathematics , inference , computer science , zero (linguistics) , linear regression , mathematical optimization , artificial intelligence , machine learning , statistics , linguistics , physics , philosophy , quantum mechanics
This article studies a variational Bayesian method to fix the linear regression (LR) model of which regressors are Gaussian distributed with non‐zero prior means, and then apply the method to the linear state space (LSS) model. Here, we innovatively transform the LSS model into a special LR model: In each state, the value obtained from the predict step can be seen as the prior mean of the regressors, and the update step can be viewed as the iterative solving in LR model with non‐zero prior means. We simulate the proposed algorithm with high‐dimensional discrete LSS models where most states are prior zeros; simulation results show that the proposed algorithm and its applications in LSS are both effective and reliable.