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Using random field theory in combination with infinite-dimensional optimization denotes an interesting new class of optimization problems with diverse application. This generally corresponds to the case when an infinite parameter is supported over a certain domain (i.e., is a function that depends on other infinite parameters). Random fields present an interesting application of this where essentially we'll have a random function ξ(d) ∈ D_ξ where d ∈ D (e.g., ξ(t) a random function of time). This would enable the use of Gaussian random fields (e.g., Gaussian processes) to be used as infinite domains.
The text was updated successfully, but these errors were encountered:
Using random field theory in combination with infinite-dimensional optimization denotes an interesting new class of optimization problems with diverse application. This generally corresponds to the case when an infinite parameter is supported over a certain domain (i.e., is a function that depends on other infinite parameters). Random fields present an interesting application of this where essentially we'll have a random function ξ(d) ∈ D_ξ where d ∈ D (e.g., ξ(t) a random function of time). This would enable the use of Gaussian random fields (e.g., Gaussian processes) to be used as infinite domains.
The text was updated successfully, but these errors were encountered: