Approximate Bayesian Recursive Estimation: On Approximation Errors

0372388 - ÚTIA 2012 CZ eng V - Research Report
Kárný, Miroslav - Dedecius, Kamil
Approximate Bayesian Recursive Estimation: On Approximation Errors.
Praha: ÚTIA AV ČR, 2012. 11 s. Research Report, 2317.
R&D Projects: GA MŠMT 1M0572; GA ČR GA102/08/0567
Institutional research plan: CEZ:AV0Z10750506
Keywords : approximate estimation * adaptive systems * recursive estimation * Kullback-Leibler divergence * forgetting
Subject RIV: BD - Theory of Information
http://library.utia.cas.cz/separaty/2012/AS/karny-approximate bayesian recursive estimation on approximation errors.pdf

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.
Permanent Link: http://hdl.handle.net/11104/0205719