Bayesian system identification

Authors:
V. Peterka
Affiliations:
Czechoslovak Academy of Sciences, Institute of Information Theory and Automation, Prague 8, Pod vodárenskou vzi 4, Czechoslovakia
Venue:
Automatica (Journal of IFAC)
Year:
1981

Citing 0
Cited 3

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Computational system identification for Bayesian NARMAX modelling

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Abstract

In Bayesian statistics the concept of probability is interpreted as a rational measure of belief which is used to describe mathematically the uncertain relation between the statistician and the external world. The statistical inference is understood as a correction of prior subjective probability distribution by objective data. The paper shows that on this Bayesian basis it is possible to build a consistent theory of system identification. The following problems are considered: one-shot and real-time identification, estimation and prediction in closed control loop, redundant and unidentifiable parameters, time-varying parameters and adaptivity.