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