Title: Comparison of Confidence Sets Designs for Various Degrees of Knowledge
Authors: Ajgl, Jiří
Straka, Ondřej
Citation: AJGL, J. STRAKA, O. Comparison of Confidence Sets Designs for Various Degrees of Knowledge. In Proceedings of the 2021 24th International Conference on Information Fusion (FUSION). Sun City: IEEE, 2021. s. 1-7. ISBN: 978-1-73774-971-4 , ISSN: neuvedeno
Issue Date: 2021
Publisher: IEEE
Document type: konferenční příspěvek
URI: 2-s2.0-85123410634
ISBN: 978-1-73774-971-4
Keywords in different language: confidence sets;estimation fusion;unknown dependence
Abstract in different language: Confidence sets are random sets constructed in such a way that the probability that they contain the estimated parameter achieves a chosen level. This paper deals with combining information from two estimates and discusses several designs with respect to various degrees of knowledge of the joint probability density function. Namely, the designs by fusion, intersection and union are considered for unknown joint density, known Gaussian joint density and Gaussian joint density with unknown cross-covariance. Evaluation criteria are proposed and the confidence sets are compared using simple numerical example.
Rights: Plný text je přístupný v rámci univerzity přihlášeným uživatelům.
Appears in Collections:Konferenční příspěvky / Conference Papers (KKY)

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Please use this identifier to cite or link to this item: http://hdl.handle.net/11025/47135

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