Original title: Approximate Bayesian Recursive Estimation: On Approximation Errors
Authors: Kárný, Miroslav ; Dedecius, Kamil
Document type: Research reports
Year: 2012
Language: eng
Series: Research Report, volume: 2317
Abstract: 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.
Keywords: adaptive systems; approximate estimation; forgetting; Kullback-Leibler divergence; recursive estimation
Project no.: CEZ:AV0Z10750506 (CEP), 1M0572 (CEP), GA102/08/0567 (CEP)
Funding provider: GA MŠk, GA ČR

Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2012/AS/karny-approximate bayesian recursive estimation on approximation errors.pdf
Original record: http://hdl.handle.net/11104/0205719

Permalink: http://www.nusl.cz/ntk/nusl-85038


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