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article · IEEE Transactions on Automatic Control · 1968

A nonlinear adaptive estimation recursive algorithm

D.G. Lainiotis

Cited by 7Abstract and details only

Abstract

A nonlinear, adaptive and recursive algorithm is derived for estimating an unknown probability density given a sequence of independent samples from the unknown density. An expansion of the unknown density in terms of a known and finite set of orthogonal functions is utilized and a Bayesian recursive learning procedure is derived for learning the coefficients of the expansion. The algorithm eliminates the need for quantization of the unknown parameter space. Adaptive estimators of population moments are also derived as known linear combinations of the recursively obtained expansion coefficients.

Cite this paper

APA 7

Lainiotis, D. (1968). A nonlinear adaptive estimation recursive algorithm. IEEE Transactions on Automatic Control, 13(2), 197–198. https://doi.org/10.1109/tac.1968.1098864

MLA 9

Lainiotis, D.G. “A nonlinear adaptive estimation recursive algorithm.” IEEE Transactions on Automatic Control, vol. 13, no. 2, 1968, pp. 197–98, https://doi.org/10.1109/tac.1968.1098864.

Chicago

Lainiotis, D.G. 1968. “A nonlinear adaptive estimation recursive algorithm.” IEEE Transactions on Automatic Control 13 (2): 197–98. https://doi.org/10.1109/tac.1968.1098864.

Harvard

Lainiotis, D. (1968) ‘A nonlinear adaptive estimation recursive algorithm’, IEEE Transactions on Automatic Control, 13(2), pp. 197–198. Available at: https://doi.org/10.1109/tac.1968.1098864.

BibTeX
@article{lainiotis1968nonlinear,
  title = {{A nonlinear adaptive estimation recursive algorithm}},
  author = {Lainiotis, D.G.},
  journal = {IEEE Transactions on Automatic Control},
  volume = {13},
  number = {2},
  pages = {197--198},
  year = {1968},
  doi = {10.1109/tac.1968.1098864}
}

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