Gaussian Process Quadrature Moment Transform

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F18%3A43952475" target="_blank" >RIV/49777513:23520/18:43952475 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/TAC.2017.2774444" target="_blank" >https://doi.org/10.1109/TAC.2017.2774444</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TAC.2017.2774444" target="_blank" >10.1109/TAC.2017.2774444</a>

Result language
angličtina
Original language name
Gaussian Process Quadrature Moment Transform
Original language description
Computation of moments of transformed random variables is a problem appearing in many engineering applications. The current methods for moment transformation are mostly based on the classical quadrature rules, which cannot account for the approximation errors. Our aim is to design a method for moment transformation of Gaussian random variables, which accounts for the error in the numerically computed mean. We employ an instance of Bayesian quadrature, called Gaussian process quadrature (GPQ), which allows us to treat the integral itself as a random variable, where the integral variance informs us about the incurred integration error. Experiments on the coordinate transformation and nonlinear filtering examples show that the proposed GPQ moment transform performs better than the classical transforms.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20205 - Automation and control systems

Project
<a href="/en/project/GA15-12068S" target="_blank" >GA15-12068S: Adaptive Approaches to State Estimation of Nonlinear Stochastic Systems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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