Locally interacting diffusions as Markov random fields on path space

Daniel Lacker, Kavita Ramanan and Ruoyu Wu

Stochastic Processes and their Applications, 2021, vol. 140, issue C, 81-114

Abstract: We consider a countable system of interacting (possibly non-Markovian) stochastic differential equations driven by independent Brownian motions and indexed by the vertices of a locally finite graph G=(V,E). The drift of the process at each vertex is influenced by the states of that vertex and its neighbors, and the diffusion coefficient depends on the state of only that vertex. Such processes arise in a variety of applications including statistical physics, neuroscience, engineering and math finance. Under general conditions on the coefficients, we show that if the initial conditions form a second-order Markov random field on d-dimensional Euclidean space, then at any positive time, the collection of histories of the processes at different vertices forms a second-order Markov random field on path space. We also establish a bijection between (second-order) Gibbs measures on (Rd)V (with finite second moments) and a set of (second-order) Gibbs measures on path space, corresponding respectively to the initial law and the law of the solution to the stochastic differential equation. As a corollary, we establish a Gibbs uniqueness property that shows that for infinite graphs the joint distribution of the paths is completely determined by the initial condition and the specifications, namely the family of conditional distributions on finite vertex sets given the configuration on the complement. Along the way, we establish approximation and projection results for Markov random fields on locally finite graphs that may be of independent interest.

Keywords: Interacting diffusions; Infinite-dimensional stochastic differential equations; Gibbs measures; Markov random fields; Gibbs measures on path space; Stochastic processes on graphs (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1016/j.spa.2021.06.007

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