By Vladimir I. Bogachev

This publication offers the reader with the vital techniques and effects regarding differential houses of measures on countless dimensional areas. within the finite dimensional case such homes are defined when it comes to densities of measures with admire to Lebesgue degree. within the endless dimensional case new phenomena come up. For the 1st time an in depth account is given of the idea of differentiable measures, initiated by way of S. V. Fomin within the Sixties; considering the fact that then the tactic has came upon many alternative very important purposes. Differentiable homes are defined for various concrete periods of measures coming up in functions, for instance, Gaussian, convex, good, Gibbsian, and for distributions of random approaches. Sobolev periods for measures on finite and countless dimensional areas are mentioned intimately. eventually, we current the most principles and result of the Malliavin calculus--a strong solution to examine smoothness houses of the distributions of nonlinear functionals on limitless dimensional areas with measures. the objective readership contains mathematicians and physicists whose learn is said to measures on countless dimensional areas, distributions of random procedures, and differential equations in limitless dimensional areas. The e-book contains an in depth bibliography at the topic.

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**Example text**

The Stratonovich integral of ξt is deﬁned as an L2 -limit T 0 n ξt ◦ dwt = (L2 ) lim δ→0 i=1 1 (ξt + ξti )(wti+1 − wti ), 2 i+1 where {ti } is a ﬁnite partition of [0, T ] and δ = maxi |ti+1 − ti |. The Stratonovich integral of the process ξt of the indicated form exists and is related with the Itˆ o integral by the equality T 0 ξt ◦ dwt = T ξt dwt + 0 1 2 T 0 ut dt. Sometimes the Stratonovich integral is called the symmetric integral. A nice feature of this integral is that it behaves like the ordinary diﬀerential under nonlinear mappings: in Itˆ o’s formula for the Stratonovich integral there is no last term.

Let us observe that H(γ) coincides also with the set of all vectors of the form h= f (x)x γ(dx), f ∈ L2 (γ). X Indeed, letting f0 be the orthogonal projection of f onto Xγ∗ in L2 (γ), we see that the integral of the diﬀerence [f (x) − f0 (x)]x over X vanishes since the integral of [f (x) − f0 (x)]l(x) vanishes for each l ∈ X ∗ . The mapping h → h establishes a linear isomorphism between H(γ) and Xγ∗ preserving the inner product. In addition, Rγ h = h. If {en } is an orthonormal basis in H(γ), then {en } is an orthonormal basis in Xγ∗ and en are independent random variables.

Tsirelson. 4. Theorem. Let γ be a centered Radon Gaussian measure on a locally convex space X, let {en } be an orthonormal basis in H(γ), and let {ξn } be independent standard Gaussian random variables (for example, the sequence of coordinate functions on IR∞ with the countable power of the standard Gaussian measure on the real line). e. ω and the distribution of its sum is γ. In particular, this is true if ξn = en . In addition, there exists a Souslin linear subspace S ⊂ X such that one has γ(S) = 1.