Approximate fusion of probability density functions using Gaussian copulas

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F23%3A43969664" target="_blank" >RIV/49777513:23520/23:43969664 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.23919/FUSION52260.2023.10224201" target="_blank" >https://doi.org/10.23919/FUSION52260.2023.10224201</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.23919/FUSION52260.2023.10224201" target="_blank" >10.23919/FUSION52260.2023.10224201</a>

Result language
angličtina
Original language name
Approximate fusion of probability density functions using Gaussian copulas
Original language description
Subjective Bayesian estimation perceives probability density functions as expert opinions. Among various rules for combining the opinions, the product and the weighted geometric mean of densities are prominent. Nevertheless, closed-form representations are scarce and non-parametric approaches often suffer from the curse of dimensionality. This paper prospects the fusion of densities represented by non-parametric marginal densities and a parametric Gaussian copula. The explicit reconstruction of the joint densities followed by an optimisation step is avoided. A cheap approximate combination is proposed instead. The combination of marginal densities is tuned by a Gaussian term, while the proposed copula parameter uses moments of the marginal densities. The presented examples illustrate the approximative nature of the approach for non-Gaussian densities and highlight some numerical issues.
Czech name
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Czech description
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Project
<a href="/en/project/GA22-11101S" target="_blank" >GA22-11101S: Tensor Decomposition in Active Fault Diagnosis for Stochastic Large Scale Systems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 2023 26th International Conference on Information Fusion
ISBN
979-8-89034-485-4
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
1-7
Publisher name
IEEE
Place of publication
Charleston, USA
Event location
Charleston, Jižní Karolína, USA
Event date
Jun 27, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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