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Research Report

Approximate Bayesian Recursive Estimation: On Approximation Errors

Kárný Miroslav, Dedecius Kamil

: ÚTIA AV ČR, (Praha 2012)

: Research Report 2317

: CEZ:AV0Z10750506

: 1M0572, GA MŠk, GA102/08/0567, GA ČR

: approximate estimation, adaptive systems, recursive estimation, Kullback-Leibler divergence, forgetting

: http://library.utia.cas.cz/separaty/2012/AS/karny-approximate bayesian recursive estimation on approximation errors.pdf

(eng): Adaptive systems rely on recursive estimation of a firmly bounded complex- ity. As a rule, they have to use an approximation of the posterior proba- bility density function (pdf), which comprises unreduced information about the estimated parameter. In recursive setting, the latest approximate pdf is updated using the learnt system model and the newest data and then ap- proximated. The fact that approximation errors may accumulate over time course is mostly neglected in the estimator design and, at most, checked ex post. The paper inspects this problem.

: BD