Stochastic processes with sample paths in reproducing kernel Hilbert spaces
By Milan Lukić, Jay H. Beder. 2001
133 citations recorded by the source corpus.
Subfields: Applied Mathematics, Finance, Mathematical Physics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Applied Mathematics
- Bharath K. Sriperumbudur
- Motonobu Kanagawa
- Dino Sejdinovic
- Philipp Hennig
- Clint Scovel
- José R. Berrendero
- Antonio Cuevas
- Gianluigi Pillonetto
In Finance
- José R. Berrendero
- Antonio Cuevas
- Dino Sejdinovic
- Houman Owhadi
- Philipp Hennig
- Motonobu Kanagawa
- Bharath K. Sriperumbudur
- Xiaohai Sun
In Mathematical Physics
- Clint Scovel
- Antonio Cuevas
- Dino Sejdinovic
- Gianluigi Pillonetto
- Houman Owhadi
- Philipp Hennig
- Motonobu Kanagawa
- Bharath K. Sriperumbudur
Citations in the corpus
- Assessing Nonlinear Granger Causality from Multivariate Time Series (2008, background): “An RKHS (HY ,k Y ) is separable, when the topological space Y is separable 444 X. Sun and kY is continuous on Y×Y [ 16 ].”
- On the Use of Reproducing Kernel Hilbert Spaces in Functional Classification (2015, background): “(a) Note that, except for trivial cases, the trajectories x of the process X ( t ) are not included, with probability one, in H ( K ); see, e.g., (Berlinet and Thomas-Agnan, 2004, p. 66) and Luki´c and Beder (2001) for details.”
- Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences (2018, methodology): “We then review Driscoll’s theorem [Driscoll, 1973, Lukić and Beder, 2001], which provides a necessary and sufficient condition for a GP sample path to lie in a given RKHS (which can be different from the RKHS associated with the GP covariance kernel) in Section 4.2.”
- Convergence properties of the expected improvement algorithm with fixed mean and covariance functions (2007, unknown): “From the probabilistic point of view, however, the event { ξ ( · ) ∈ H} almost never happens according to Driscoll’s theorem (Lukic and Beder, 2001). paper states The second result of this that the result of Theorem 6 also holds P -almost surely in Ω .”
- A new kernel-based approach for linear system identification (2010, background): “For known y and γ, the minimum variance estimate of f is given by f̂ = arg min g∈H⊕B n∑ i=1 (yi − Lui [g]) 2 + γ‖P[g]‖2H (4) Remark 2 The above proposition states the duality between Gaussian processes and RKHS [48,26], which will be further exploited in the sequel.”
- Separability of reproducing kernel spaces (2015, background): “For example, Steinwart and Christmann’s [33, Thm. 7.22] oracle inequality for SVMs, Christmann and Steinwart [8, Thms. 7 & 12], [7], [6], Steinwart and Christmann [34], [35], De Vito, Rosasco and Toigo [9], Hable and Christmann [18, Thm. 3.2], Lukić and Beder [25], Steinwart [32] and Vovk [38, Thm.”