Error analysis in Bayesian identification of non-linear state-space models
Abstract
In the last two decades, several methods based on sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC) have been proposed for Bayesian identification of stochastic non-linear state-space models (SSMs). It is well known that the performance of these simulation based identification methods depends on the numerical approximations used in their design. We propose the use of posterior Cram\'er-Rao lower bound (PCRLB) as a mean square error (MSE) bound. Using PCRLB, a systematic procedure is developed to analyse the estimates delivered by Bayesian identification methods in terms of bias, MSE, and efficiency. The efficacy and utility of the proposed approach is illustrated through a numerical example.
Keywords
Cite
@article{arxiv.1307.6254,
title = {Error analysis in Bayesian identification of non-linear state-space models},
author = {Aditya Tulsyan and Biao Huang and R. Bhushan Gopaluni and J. Fraser Forbes},
journal= {arXiv preprint arXiv:1307.6254},
year = {2013}
}
Comments
This article has been published in: Tulsyan, A, B. Huang, R.B. Gopaluni and J.F. Forbes (2013). Bayesian identification of non-linear state-space models: Part II- Error Analysis. In: Proceedings of the 10th IFAC International Symposium on Dynamics and Control of Process Systems. Mumbai, India