Add to ORKGDynamic systems under action of small random disturbances, spectral analysis on the base of graph theory are investigated in the paper aiming at the Markovity proof of originating subprocesses in exponentially large scales of time. As a result the asymphotics of exponentially small series of own values and corresponding own functions has been obtained.It has been shown, that in suitable scales of time the evolution is described by Markovian processes with the finite number of states. Characteristics of these processes have been calculated. The paper results make it possible to describe the line form in theory of superparamagnetism. The methods developed may be used in the analysis of diffusion effects of arbitrary natureAvailable from VNTIC...
Abstract. We study Markov processes where the “time ” parameter is replaced by paths in a directed g...
We study a large-time limit of a Markov process whose states are finite graphs. The number of the ve...
It is well-known that compositions of Markov processes with inverse subordinators are governed by i...
Nonlinear Markov chains are probabilistic models commonly used in physics, biology, and the social s...
Markovian chains with random generators and initial distributions and their averaged correlators are...
In this paper the authors analyze the long time behavior of certain Markov chains, namely jump proce...
Nonlinear Markov chains are probabilistic models commonly used in physics, biology, and the social s...
We consider long-time behavior of dynamical systems perturbed by a small noise. Under certain condit...
A straightforward algorithm for the multiple time scale decomposition of singularly perturbed Markov...
In this paper, we outline a model of graph (or network) dynamics based on two ingredients. The first...
In this paper we continue the investigation of the spectral theory and exponential asymp-totics of p...
In this paper, we outline a model of graph (or network) dynamics based on two ingredients. The first...
In this paper, we outline a model of graph (or network) dynamics based on two ingredients. The first...
We study the large-time dynamics of a Markov process whose states are finite directed graphs. The nu...
In this paper we continue the investigation of the spectral theory and exponential asymptotics of pr...
Abstract. We study Markov processes where the “time ” parameter is replaced by paths in a directed g...
We study a large-time limit of a Markov process whose states are finite graphs. The number of the ve...
It is well-known that compositions of Markov processes with inverse subordinators are governed by i...
Nonlinear Markov chains are probabilistic models commonly used in physics, biology, and the social s...
Markovian chains with random generators and initial distributions and their averaged correlators are...
In this paper the authors analyze the long time behavior of certain Markov chains, namely jump proce...
Nonlinear Markov chains are probabilistic models commonly used in physics, biology, and the social s...
We consider long-time behavior of dynamical systems perturbed by a small noise. Under certain condit...
A straightforward algorithm for the multiple time scale decomposition of singularly perturbed Markov...
In this paper, we outline a model of graph (or network) dynamics based on two ingredients. The first...
In this paper we continue the investigation of the spectral theory and exponential asymp-totics of p...
In this paper, we outline a model of graph (or network) dynamics based on two ingredients. The first...
In this paper, we outline a model of graph (or network) dynamics based on two ingredients. The first...
We study the large-time dynamics of a Markov process whose states are finite directed graphs. The nu...
In this paper we continue the investigation of the spectral theory and exponential asymptotics of pr...
Abstract. We study Markov processes where the “time ” parameter is replaced by paths in a directed g...
We study a large-time limit of a Markov process whose states are finite graphs. The number of the ve...
It is well-known that compositions of Markov processes with inverse subordinators are governed by i...