Quo vadis, Bayesian identification?

✍ Scribed by Rudolf Kulhavý; Petya Ivanova

• Publisher: John Wiley and Sons
• Year: 1999
• Tongue: English
• Weight: 142 KB
• Volume: 13
• Category: Article
• ISSN: 0890-6327
• DOI: 10.1002/(sici)1099-1115(199908)13:6<469::aid-acs555>3.0.co;2-o

✦ Synopsis

The Bayesian identi"cation 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.