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pro vyhledávání: '"Bay, Xavier"'
Autor:
Bay, Xavier, Croix, Jean-Charles
The study of Gaussian measures on Banach spaces is of active interest both in pure and applied mathematics. In particular, the spectral theorem for self-adjoint compact operators on Hilbert spaces provides a canonical decomposition of Gaussian measur
Externí odkaz:
http://arxiv.org/abs/1704.01448
Autor:
Maatouk, Hassan, Bay, Xavier
Physical phenomena are observed in many fields (sciences and engineering) and are often studied by time-consuming computer codes. These codes are analyzed with statistical models, often called emulators. In many situations, the physical system (compu
Externí odkaz:
http://arxiv.org/abs/1606.01265
In this paper, we extend the correspondence between Bayes' estimation and optimal interpolation in a Reproducing Kernel Hilbert Space (RKHS) to the case of linear inequality constraints such as boundedness, monotonicity or convexity. In the unconstra
Externí odkaz:
http://arxiv.org/abs/1602.02714
Gaussian Processes (GPs) are a popular approach to predict the output of a parameterized experiment. They have many applications in the field of Computer Experiments, in particular to perform sensitivity analysis, adaptive design of experiments and g
Externí odkaz:
http://arxiv.org/abs/1602.00853
Publikováno v:
Computational Statistics; Jun2024, Vol. 39 Issue 4, p1779-1806, 28p
Akademický článek
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Due to their flexibility Gaussian processes are a well-known Bayesian framework for nonparametric function estimation. Integrating inequality constraints, such as monotonicity, convexity, and boundedness, into Gaussian process models significantly im
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::b0f4f5563d50e51917dca6d310257adc
https://hal.science/hal-04084865/document
https://hal.science/hal-04084865/document
In this paper, we extend the correspondence between Bayesian estima- tion and optimal smoothing in a Reproducing Kernel Hilbert Space (RKHS) by adding convex constraints to the problem. Through a sequence of approxi- mating Hilbertian subspaces and a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2885::1173d8814e4a4a67888f682223763e81
https://hal.science/hal-03282857
https://hal.science/hal-03282857
Publikováno v:
A. Hinrichs, P. Kritzer, F. Pillichshammer (eds.). Monte Carlo and Quasi-Monte Carlo Methods 2022. Springer Verlag
A. Hinrichs, P. Kritzer, F. Pillichshammer (eds.). Monte Carlo and Quasi-Monte Carlo Methods 2022. Springer Verlag, In press
A. Hinrichs, P. Kritzer, F. Pillichshammer (eds.). Monte Carlo and Quasi-Monte Carlo Methods 2022. Springer Verlag, In press
International audience; Gaussian processes have become essential for non-parametric function estimation and widely used in many fields like machine learning. In this paper, large scale Gaussian process regression (GPR) is investigated. This problem i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::dae021a172ff7964d6a4b9fe3307e6ca
https://hal.science/hal-03909542v2
https://hal.science/hal-03909542v2
Akademický článek
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