Approximate fusion of probability density functions using Gaussian copulas

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Approximate fusion of probability density functions using Gaussian copulas

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Approximate fusion of probability density functions using Gaussian copulas

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/GA22-11101S" target="_blank" >GA22-11101S: Tenzorový rozklad v aktivní diagnostice poruch pro stochastické rozlehlé systémy</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 2023 26th International Conference on Information Fusion

ISBN

979-8-89034-485-4

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

1-7

Název nakladatele

IEEE

Místo vydání

Charleston, USA

Místo konání akce

Charleston, Jižní Karolína, USA

Datum konání akce

27. 6. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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