On Interacting Systems of Hilbert-Space-Valued Diffusions

A nonlinear Hilbert-space-valued stochastic differential equation where L ^-1 (L being the generator of the evolution semigroup) is not nuclear is investigated in this paper. Under the assumption of nuclearity of L ^-1 , the existence of a unique solution lying in the Hilbert space H has been shown by Dawson in an early paper. When L ^-1 is not nuclear, a solution in most cases lies not in H but in a larger Hilbert, Banach, or nuclear space. Part of the motivation of this paper is to prove under suitable conditions that a unique strong solution can still be found to lie in the space H itself. Uniqueness of the weak solution is proved without moment assumptions on the initial random variable. A second problem considered is the asymptotic behavior of the sequence of empirical measures determined by the solutions of an interacting system of H -valued diffusions. It is shown that the sequence converges in probability to the unique solution Λ ₀ of the martingale problem posed by the corresponding McKean—Vlasov equation. Accepted 4 April 1996     

On the McKean-Vlasov Limit for Interacting Diffusions

For a system of diffusions in a domain of R^d with long-range weak interaction the behavior of the associated empirical process is studied. Under mild growth and smoothness assumptions on the drift and diffusion coefficients such as coercivity and monotonicity conditions the law of large numbers and the propagation of chaos are proved. Existence and uniqueness of the weak solution to the McKean - Vlasov equation and the associated non-linear martingale problem are investigated.     

Large Deviations for Diffusions Interacting Through Their Ranks

We prove a large deviations principle (LDP) for systems of diffusions (particles) interacting through their ranks when the number of particles tends to infinity. We show that the limiting particle density is given by the unique solution of the appropriate McKean‐Vlasov equation and that the corresponding cumulative distribution function evolves according to a nondegenerate generalized porous medium equation with convection. The large deviations rate function is provided in explicit form. This is the first instance of an LDP for interacting diffusions where the interaction occurs both through the drift and the diffusion coefficients and where the rate function can be given explicitly. In the course of the proof, we obtain new regularity results for tilted versions of such a generalized porous medium equation.© 2016 Wiley Periodicals, Inc.     

Measure-Valued Diffusions and Continual Systems of Interacting Particles in a Random Medium

We consider continual systems of stochastic equations describing the motion of a family of interacting particles whose mass can vary in time in a random medium. It is assumed that the motion of every particle depends not only on its location at given time but also on the distribution of the total mass of particles. We prove a theorem on unique existence, continuous dependence on the distribution of the initial mass, and the Markov property. Moreover, under certain technical conditions, one can obtain the measure-valued diffusions introduced by Skorokhod as the distributions of the mass of such systems of particles. __________ Translated from Ukrains'kyi Matematychnyi Zhurnal, Vol. 57, No. 9, pp. 1289–1301, September, 2005.     

Tanaka Formula and Local Time for a Class of Interacting Branching Measure-valued Diffusions

We construct superprocesses with dependent spatial motion(SDSMs) in Euclidean spaces R^d with d≥1 and show that,even when they start at some unbounded initial positive Radon measure such as Lebesgue measure on R^d,their local times exist when d≤3.A Tanaka formula of the local time is also derived.     

Finite and Infinite Systems of Interacting Diffusions: Cluster Formation and Universality Properties

We study some aspects of the relationship between the long time behaviour of systems with a finite but large number of components and their idealizations with countably many components. The following class of models is considered in detail, which contains examples occuring in population growth and population genetic models.     

Empirical Measure and Small Noise Asymptotics Under Large Deviation Scaling for Interacting Diffusions

Journal of Theoretical Probability - Consider a collection of particles whose state evolution is described through a system of interacting diffusions in which each particle is driven by an...     

A Note on Wavelets and Diffusions

Motivated by image processing and numerical wavelet methods for partial differential equations, we study the theoretical interactions between wavelets and the diffusion equations. Important properties of wavelets, such as the translation and scaling invariance, the p-vanishing-moment condition, and the atomic decomposition, are integrated into the diffusion process and lead to many interesting results.     

Harmonic Analysis and Degenerate Diffusions on Euclidean Groups

We take up an idea, introduced by Chirikjian andKyatkin, of analyzing a family of left-invariant diffusion equations on Euclidean groups via the group Fourier transform. These diffusion equations model the probability distribution of the orientation in space of certain polymers in solution, including DNA. We study the evolution equations satisfied by the Fourier coefficients of such a solution. Our main task here is to estimate these Fourier coefficients sufficiently well that one canestimate the error in truncating the group inverse Fourier transform to afinite region.     

Optimal Control of Diffusions

We prove a version of Pontryagin's maximum principle for time and norm optimal control of linear diffusion processes. This result includes both necessary and sufficient conditions and implies a ``concentration principle' for the optimal measure-valued controls.     

Singularly Perturbed Switching Diffusions: Rapid Switchings and Fast Diffusions

Motivated by many problems in optimization and control, this paper is concerned with singularly perturbed systems involving both diffusions and pure jump processes. Two models are treated. In the first model, the jump process changes very rapidly by comparison with the diffusion processes. In the second model, the diffusions change rapidly in comparison with the jump process. Asymptotic expansions are developed for the transition density vectors via a constructive method; justification of the asymptotic expansions and analysis of the remainders are provided.     

A New Markov Selection Procedure for Degenerate Diffusions

A new selection principle is proposed for construction of a Feller solution to an ill-posed degenerate diffusion process. This is based on constructing the Feller transition kernel from the unique (under suitable conditions) continuous viscosity solutions to the backward Kolmogorov equation associated with the diffusion. The connection of this Feller process with the small-noise limits of the nondegenerate perturbations of the diffusion and several other related phenomena are also discussed.     

Diffusions of multiplicative cascades

A multiplicative cascade can be thought of as a randomization of a measure on the boundary of a tree, constructed from an iid collection of random variables attached to the tree vertices. Given an initial measure with certain regularity properties, we construct a continuous time, measure-valued process whose value at each time is a cascade of the initial one. We do this by replacing the random variables on the vertices with independent increment processes satisfying certain moment assumptions. Our process has a Markov property: at any given time it is a cascade of the process at any earlier time by random variables that are independent of the past. It has the further advantage of being a martingale and, under certain extra conditions, it is also continuous. For Gaussian independent increment processes we develop the infinite-dimensional stochastic calculus that describes the evolution of the measure process, and use it to compute the optimal Hölder exponent in the Wasserstein distance on measures. We also discuss applications of this process to the model of tree polymers.     

A Combinatorial Approach to Piecewise Linear Time Series Analysis

Over recent years, several nonlinear time series models have been proposed in the literature. One model that has found a large number of successful applications is the threshold autoregressive model (TAR). The TAR model is a piecewise linear process whose central idea is to change the parameters of a linear autoregressive model according to the value of an observable variable, called the threshold variable. If this variable is a lagged value of the time series, the model is called a self-exciting threshold autoregressive (SETAR) model. In this article, we propose a heuristic to estimate a more general SETAR model, where the thresholds are multivariate. We formulate the task of finding multivariate thresholds as a combinatorial optimization problem. We develop an algorithm based on a greedy randomized adaptive search procedure (GRASP) to solve the problem. GRASP is an iterative randomized sampling technique that has been shown to quickly produce good quality solutions for a wide variety of optimization problems. The proposed model performs well on both simulated and real data.     

One-Dimensional Diffusions and Approximation

Consider the Voronovskaja operator A of a sequence of positive linear operators and let u(t, x) be the solution of the Cauchy problem for A. In the spirit of Altomare’s theory this solution can be studied by using the semigroup (T(t))_{t ≥ 0} generated by A and represented in terms of the operators L_n.One associates to A a stochastic equation; its solution can be also used in order to represent u(t, x).The relations between all these objects are described in the case of the operator A associated with some Meyer-König and Zeller type operators.     

Data Augmentation for Diffusions

The problem of formal likelihood-based (either classical or Bayesian) inference for discretely observed multidimensional diffusions is particularly challenging. In principle, this involves data augmentation of the observation data to give representations of the entire diffusion trajectory. Most currently proposed methodology splits broadly into two classes: either through the discretization of idealized approaches for the continuous-time diffusion setup or through the use of standard finite-dimensional methodologies discretization of the diffusion model. The connections between these approaches have not been well studied. This article provides a unified framework that brings together these approaches, demonstrating connections, and in some cases surprising differences. As a result, we provide, for the first time, theoretical justification for the various methods of imputing missing data. The inference problems are particularly challenging for irreducible diffusions, and our framework is correspondingly more complex in that case. Therefore, we treat the reducible and irreducible cases differently within the article. Supplementary materials for the article are available online.     

Terminal Invariance of Jump Diffusions

Doklady Mathematics - Terminal invariance sufficient conditions for nonlinear dynamical stochastic controllable systems of diffusion-jump type are proposed. These conditions have no analogues in...