Random fields on Hilbert spaces with their equivalent Gaussian measures

We consider the infinite dimensional case for Gaussian fields having a compact set of Rd as their index set. For Hilbert space valued Gaussian fields, we prove conditions for equivalence of their Gaussian measures, in the sense that the Gaussian measures induced by two given fields are equivalent on their paths. Such conditions are proved to depend on the difference between the correlation operators that characterize the two Gaussian measures. Further, we provide the analogue of the celebrated Bochner’s characterisation for correlation operators that represents the infinite dimensional extension of matrix-valued kernels.

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Publication Details

Journal
Bernoulli
Published
2025-04-09
DOI
https://doi.org/10.3150/24-bej1757
Citations
1
Primary Topic
Stochastic processes and financial applications
Type
article
Field-Weighted Citation Impact
2.21
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article

Random fields on Hilbert spaces with their equivalent Gaussian measures

Emilio Porcu, Vinícius Ferreira, Jorge P. Zubelli
1 citations
Bernoulli
Stochastic processes and financial applications
2.21
article

Random fields on Hilbert spaces with their equivalent Gaussian measures

Emilio Porcu, Vinícius Ferreira, Jorge P. Zubelli
article en
1 citations

Abstract

We consider the infinite dimensional case for Gaussian fields having a compact set of Rd as their index set. For Hilbert space valued Gaussian fields, we prove conditions for equivalence of their Gaussian measures, in the sense that the Gaussian measures induced by two given fields are equivalent on their paths. Such conditions are proved to depend on the difference between the correlation operators that characterize the two Gaussian measures. Further, we provide the analogue of the celebrated Bochner’s characterisation for correlation operators that represents the infinite dimensional extension of matrix-valued kernels.

BernoulliVol. 31(3)
Khalifa University of Science and Technology (AE)
Reduced inequalities
Openalex Percentile: Top 16%
Stochastic processes and financial applications
2.21
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