Inference for an anisotropic diffusion model
The vector sum of a white noise in an unknown hyperspace and an Ornstein-Uhlenbeck process in an unknown line is observed through sharp linear test functions over a finite time span. The parameters associated with the white noise (including the hyperplane) are determinable with precision and index the measure-equivalence classes in the relevant sample space. An intraclass relative density provides a basis for Bayesian inference of the remaining parameters.
Year of publication: |
1976
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Authors: | Eaves, David |
Published in: |
Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 6.1976, 1, p. 65-80
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Publisher: |
Elsevier |
Keywords: | Diffusion White noise Ornstein-Uhlenbeck Equivalence classes Bayesian |
Saved in:
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