. "K\u00E1rn\u00FD, Miroslav" . "285" . . "Approximate Bayesian recursive estimation" . "10.1016/j.ins.2014.01.048" . . . "Approximate Bayesian recursive estimation"@en . . "3922" . . . "RIV/67985556:_____/14:00425539!RIV15-GA0-67985556" . . "Approximate Bayesian recursive estimation" . "Information Sciences" . "I, P(GA13-13502S)" . . . "RIV/67985556:_____/14:00425539" . . . "1"^^ . "[7B2AE885BAB4]" . "1"^^ . "Approximate parameter estimation; Bayesian recursive estimation; Kullback\u2013Leibler divergence; Forgetting"@en . "12"^^ . . "0020-0255" . "Bayesian learning provides a firm theoretical basis of the design and exploitation of algorithms in data-streams processing (preprocessing, change detection, hypothesis testing, clustering, etc.). Primarily, it relies on a recursive parameter estimation of a firmly bounded complexity. As a rule, it has to approximate the exact posterior probability density (pd), which comprises unreduced information about the estimated parameter. In the recursive treatment of the data stream, the latest approximate pd is usually updated using the treated parametric model and the newest data and then approximated. 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 the estimator design with respect to the error accumulation and concludes that a sort of forgetting (pd flattening) is an indispensable part of a reliable approximate recursive estimation."@en . "1" . . . . "000342540700007" . "Bayesian learning provides a firm theoretical basis of the design and exploitation of algorithms in data-streams processing (preprocessing, change detection, hypothesis testing, clustering, etc.). Primarily, it relies on a recursive parameter estimation of a firmly bounded complexity. As a rule, it has to approximate the exact posterior probability density (pd), which comprises unreduced information about the estimated parameter. In the recursive treatment of the data stream, the latest approximate pd is usually updated using the treated parametric model and the newest data and then approximated. 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 the estimator design with respect to the error accumulation and concludes that a sort of forgetting (pd flattening) is an indispensable part of a reliable approximate recursive estimation." . . "US - Spojen\u00E9 st\u00E1ty americk\u00E9" . "Approximate Bayesian recursive estimation"@en .