Paley–Wiener integral – Wikipedia
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In mathematics, the Paley–Wiener integral is a simple stochastic integral. When applied to classical Wiener space, it is less general than the Itō integral, but the two agree when they are both defined.
The integral is named after its discoverers, Raymond Paley and Norbert Wiener.
Definition[edit]
Let
be an abstract Wiener space with abstract Wiener measure
on
. Let
be the adjoint of
. (We have abused notation slightly: strictly speaking,
, but since
is a Hilbert space, it is isometrically isomorphic to its dual space
, by the Riesz representation theorem.)
It can be shown that
is an injective function and has dense image in
.[citation needed] Furthermore, it can be shown that every linear functional
is also square-integrable: in fact,
This defines a natural linear map from
to
, under which
goes to the equivalence class
of
in
. This is well-defined since
is injective. This map is an isometry, so it is continuous.
However, since a continuous linear map between Banach spaces such as
and
is uniquely determined by its values on any dense subspace of its domain, there is a unique continuous linear extension
of the above natural map
to the whole of
.
This isometry
is known as the Paley–Wiener map.
, also denoted
, is a function on
and is known as the Paley–Wiener integral (with respect to
).
It is important to note that the Paley–Wiener integral for a particular element
is a function on
. The notation
does not really denote an inner product (since
and
belong to two different spaces), but is a convenient abuse of notation in view of the Cameron–Martin theorem. For this reason, many authors[citation needed] prefer to write
or
rather than using the more compact but potentially confusing
notation.
See also[edit]
Other stochastic integrals:
References[edit]
- Park, Chull; Skoug, David (1988), “A Note on Paley-Wiener-Zygmund Stochastic Integrals”, Proceedings of the American Mathematical Society, 103 (2): 591–601, doi:10.1090/S0002-9939-1988-0943089-8, JSTOR 2047184
- Elworthy, David (2008), MA482 Stochastic Analysis (PDF), Lecture Notes, University of Warwick (Section 6)
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