SearcharxivSearch

arXiv · 2408.15369

Gibbs scheme in the theory of random fields

Abstract

The purpose of this work is to expand and clarify the concept of the class of Gibbs random fields and give its structure the form accepted in the theory of random processes. It is possible thanks to the proposed purely probabilistic definition of the Gibbs random field without refereing to any physical notion. However, we do not oppose to each other the physical and probabilistic points of view on mathematical statistical physics; on the contrary, we show their natural compatibility within the framework of the suggested Gibbs scheme. The outlines of the corresponding theory are presented. In this theory, the DLR--definition is not used. At the same time, the related existence theorem provides one of adequate ways to construct Gibbs random fields (in the sense of probabilistic definition). The results of mathematical statistical physics are embedded in the theory of Gibbs random fields under development as its important (if not the most important) part. We describe in general terms the range of those problems that are more natural to solve using our definition. For example, the problem of the Gibbsian description of known classes of random fields as well as questions of validity of limit theorems of probability theory.

Explore related subjects

Keep this discovery

BibTeXRIS

L. A. Khachatryan, B. S. Nahapetian. 2024-08-27. Gibbs scheme in the theory of random fields. https://doi.org/10.1007/s00023-025-01573-z

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Averaging principles for nonautonomous multiscale stochastic Burgers equations with reflection

In this paper, we study averaging principles for nonautonomous multiscale stochastic Burgers equations with reflection. First, we derive a general averaging principle applicable to such equations under minimal assumptions. Subsequently, since the coefficients of the obtained averaged equation still depend on the small scaling parameter $\e$, we impose either periodic or asymptotic conditions on the coefficients, thereby obtain two distinct averaged equations whose coefficients are independent of $\e$ and establish two averaging principles. Stopping times and Khasminskii's time discretization schemes play an important role. Finally, a concrete example is provided to illustrate the applicability and validity of the theoretical results.

math.PR

Spectral properties of Random Matrices

We give the theoretical foundations of random matrix theory through the definitions of a random matrix, a random probability measure and the corresponding empirical spectral distribution. The technical tool we use is the Stieltjes transform method through which we prove optimal convergence of the empirical spectral distribution of random sample covariance matrices to the deterministic Marchenko-Pastur distribution. We also give new results about the rigidity of the eigenvalues of this random sample covariance matrix and the rate of their convergence. We then define the Dyson equation method to prove new local laws about a random matrix model that interpolates between the Marchenko-Pastur distribution, the elliptical law and the circular law. Through our work these local laws can be considered universal.

math.PR

Moments approach for the elephant random walk

We discuss the method of moments for the one-dimensional elephant random walk (ERW). We first derive a differential recurrence relation for the characteristic function of the ERW, which yields a corresponding system of recurrence relations for its moments. We then obtain asymptotic approximations for the moments in each of the three parameter regimes of the ERW. Finally, by establishing the convergence of the moments and verifying the corresponding moment-determinacy conditions, we identify the limiting distributions of the ERW in each regime.

math.PR