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	On-line nonlinear estimation Kulhavý, Rudolf The Bayesian identification of complex models is known to require extensive computer resources. Practical implementation requires approximation of the theoretically optimal solution. The paper discusses three major approaches to approximate Bayesian estimation-local weighting of data, reduction of model family to representative points and minimum relative entropy restoration of the information divergence of the empirical and model distributions of data. Detailed record
	Statistical dependences in IRd: An information-theoretic approach Darbellay, Georges A. Detailed record
	Quasi-Bayes estimation applied to normal mixture Kárný, Miroslav ; Kadlec, Jiří ; Sutanto, E. L. Detailed record
	Markov chains in non-linear identification: Bayesian clustering for reduced parametrisation Valečková, Markéta ; Kárný, Miroslav Detailed record
	An algorithm decreasing mutual information Šindelář, Jan ; Kárný, Miroslav Detailed record
	Model-based fault detection and isolation methods based on Bayesian bootstrap filter Tesař, Ludvík Detailed record
	A sequential stopping rule for extensive simulations Rojíček, Jiří ; Kárný, Miroslav Detailed record
	Towards Bayesian pooling of probabilistic controllers Kárný, Miroslav ; Šindelář, Jan ; Valečková, Markéta Detailed record

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