Quo vadis, Bayesian identification?
Rudolf Kulhavý, Petya Ivanova · International Journal of Adaptive Control and Signal Processing · 1999
The Bayesian identification of non-linear, non-Gaussian, non-stationary or non-parametric models is notoriously known as computer-intensive and not solvable in a closed form. The paper outlines three major approaches to approximate Bayesian estimation, based on locally weighted smoothing of data, iterative and non-iterative Monte Carlo simulation and direct approximation of an information ‘distance’ between the empirical and model distributions of data. The information-based view of estimation is used throughout to give more insight into the methods and show their mutual relationship. Copyright © 1999 John Wiley & Sons, Ltd.