QUANTUM GAUSSIAN PROCESSES

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Representation of gaussian processes equivalent to a gaussian martingalet

This paper states that a Gaussian process Y with mean 0 is equivalent to a Gaussian martingale starting from 0 if and only if Y is a semi-martingale with Gaussian martingale part and Gaussian “clrift” of a particular kind. We also obtain a theorem of Girsanov type tor Gaussian martingales and a criterion for the equivalence mentioned above in more convenient terms. Our results extend those of M. Hitsuda [8] concerning equivalence to a Wiener process     

Hellinger integrals of gaussian processes with independent increments

The aim of this paper is to calculate the Hellinger integral of the distribution laws of Gaussian processes with independent increments. As an application, necessary and sufficient conditions for the equivalence of the distribution laws are derived. Furthermore estimations for the variational distance of the distribution laws are given.     

An application of the neyman-pearson lemma to gaussian processes

Letξ _i (t),i=1,2,t ∈ [0, 1], be Gaussian zero mean processes with continous sample paths. Bounds for the probabilitiesβ _i =P{ξ _i -α _i ∈B},i=1,2, are given, where a_iε C0, 1., and B is a Borel subset of C[0, 1] Bibliography: 5 titles. Translated fromZapiski Nauchnykh Seminarov POMI, Vol. 207, pp. 5–12, 1993. Translated by V. Sudakov.     

High extrema of Gaussian chaos processes

Let ξ ( t)=(ξ ₁(t),…,ξ _d (t)) be a Gaussian stationary vector process. Let \(g:{\mathbb {R}}^{d}\rightarrow {\mathbb {R}}\) be a homogeneous function. We study probabilities of large extrema of the Gaussian chaos process g(ξ(t)). Important examples include \(g(\mathbf {\boldsymbol {\xi }}(t))={\prod }_{i=1}^{d}\xi _{i}(t)\) and \(g(\mathbf {\boldsymbol {\xi }}(t))={\sum }_{i=1}^{d}a_{i}{\xi _{i}^{2}}(t)\). We review existing results partially obtained in collaboration with E. Hashorva, D. Korshunov, and A. Zhdanov. We also present the principal methods of our investigations which are the Laplace asymptotic method and other asymptotic methods for probabilities of high excursions of Gaussian vector process’ trajectories.     

Large extremes of Gaussian chaos processes

We study probabilities of large extremes of Gaussian chaos processes, that is, homogeneous functions of Gaussian vector processes. Important examples are products of Gaussian processes and quadratic forms of them. Exact asymptotic behaviors of the probabilities are found. To this aim, we use joint results of E. Hashorva, D. Korshunov and the author on Gaussian chaos, as well as a substantially modified asymptotical Double Sum Method.     

On the differentiability of a class of stationary gaussian processes

For a stationary Gaussian process either almost all sample paths are almost everywhere differentiable or almost all sample paths are almost nowhere differentiable. In this paper it is shown by means of an example involving a random lacunary trigonometric series that “almost everywhere differentiable” and “almost nowhere differentiable” cannot in general be replaced by “everywhere differentiable” and “nowhere differentiable”, respectively.     

Path and semimartingale properties of chaos processes

The present paper characterizes various properties of chaos processes which in particular include processes where all time variables admit a Wiener chaos expansion of a fixed finite order. The main focus is on the semimartingale property, p$p$-variation and continuity. The general results obtained are finally used to characterize when a moving average is a semimartingale.     

Density minoration of a strongly non-degenerated random variable

We obtain a lower bound for the density of a d-dimensional random variable on the Wiener space under exponential moment condition of the divergence of covering vector fields.     

Characterization of vector valued,gaussian, stationary,markov processes

We extend an old result by Doob characterizing real-valued, Gaussian, stationary, Markov processes to the vector case. In this case a deterministic component appears that consists of a system of harmonic oscillators while the random part is a collection of independent oscillator processes, modulo linear changes of coordinates.     

Densité de points et minoration de hauteur

We obtain a lower bound for the normalised height of a non-torsion subvariety V of a C.M. abelian variety. This lower bound is optimal in terms of the geometric degree of V, up to a power of a “log”. We thus extend the results of Amoroso and David on the same problem on a multiplicative group . We prove furthermore that the optimal lower bound (conjectured by David and Philippon) is a corollary of the conjecture of David and Hindry on the abelian Lehmer's problem. We deduce these results from a density theorem on the non-torsion points of V.     

Occupation densities for stochastic integral processes in the second Wiener chaos

Summary The two-point distributions of Skorohod integral processes in the second Wiener chaos are mainly described by a Hilbert-Schmidt operatorT giving the mutual interaction of infinitely many Gaussian components and by simple multiplication operators. So are the Fourier transforms of their occupation measures. This enables us to use the well known Fourier analytic criterion discovered and elaborated by Berman to derive integral conditions for the existence of their occupation densities in terms of associated Hilbert-Schmidt operators. IfT is a trace class operator, we get a necessary and sufficient criterion, if it is not, still a sufficient one. In a case in which the interaction is particularly simple, we verify the appropriate integral condition and show that the results are essentially beyond the reach of enlargement of filtrations techniques of semimartingale theory.     

A gaussian upper bound for gaussian multi-stage stochastic linear programs

This paper deals with two-stage and multi-stage stochastic programs in which the right-hand sides of the constraints are Gaussian random variables. Such problems are of interest since the use of Gaussian estimators of random variables is widespread. We introduce algorithms to find upper bounds on the optimal value of two-stage and multi-stage stochastic (minimization) programs with Gaussian right-hand sides. The upper bounds are obtained by solving deterministic mathematical programming problems with dimensions that do not depend on the sample space size. The algorithm for the two-stage problem involves the solution of a deterministic linear program and a simple semidefinite program. The algorithm for the multi-stage problem invovles the solution of a quadratically constrained convex programming problem.     

Lagrangian chaos and multiphase processes in vortex flows

We discuss experimental and numerical studies of the effects of Lagrangian chaos (chaotic advection) on the stretching of a drop of an immiscible impurity in a flow. We argue that the standard capillary number used to describe this process is inadequate since it does not account for advection of a drop between regions of the flow with varying velocity gradient. Consequently, we propose a Lagrangian-generalized capillary number C_L number based on finite-time Lyapunov exponents. We present preliminary tests of this formalism for the stretching of a single drop of oil in an oscillating vortex flow, which has been shown previously to exhibit Lagrangian chaos. Probability distribution functions (PDFs) of the stretching of this drop have features that are similar to PDFs of C_L. We also discuss on-going experiments that we have begun on drop stretching in a blinking vortex flow.     

Large deviations and the propagation of chaos for Schrödinger processes

Summary Schrödinger processes due to Schrödinger (1931) (the definition of which is given in Sect. 4) are uniquely characterized by a large deviation principle, in terms of the relative entropy with respect to a reference process, which is a renormalized diffusion process with creation and killing in applications. Anapproximate Sanov property of a subsetA _a,b is shown, whereA _a,b denotes the set of all probability measures on a path space with prescribed marginal distributions {q _a, q_b} at finite initial and terminal timesa andb, respectively. It is shown that there exists the unique Markovian modification ofn-independent copies of renormalized processes conditioned by the empirical distribution, and that the propagation of chaos holds for the system of interacting particles with the Schrödinger process as the limiting distribution.