Institute of Information Theory and Automation

You are here

Bibliography

Journal Article

Recursive Bayesian estimation of autoregressive model with uniform noise using approximation by parallelotopes

Pavelková Lenka, Jirsa Ladislav

: International Journal of Adaptive Control and Signal Processing vol.31, 8 (2017), p. 1184-1192

: 7D12004, GA MŠk

: approximate parameter estimation, ARX model, Bayesian estimation, bounded noise, Kullback-Leibler divergence, parallelotope

: 10.1002/acs.2756

: http://library.utia.cas.cz/separaty/2017/AS/pavelkova-0472081.pdf

(eng): This paper proposes a recursive algorithm for the estimation of a stochastic autoregressive model with an external input. The noise of the involved model is described by a uniform distribution. The model parameters are estimated using the Bayesian approach. Without an approximation, the support of the posterior distribution is a complex multidimensional polytope whose number of faces increases with time. We propose an approximation of this polytope in each time step by a parallelotope with a constant number of faces. The behaviour of the proposed algorithm is illustrated by simulations and compared with other methods.

: BC

: 10201

2019-01-07 08:39