Markov Processes And Related Fields

The journal ”Markov Processes and Related Fields” was founded in 1994 by a brilliant Russian mathematician V. A. Malyshev (1938 – 2022), who remained to be the Editor-in-Chief of this journal till his last day.

Markov processes is a well-established classical field in Probability theory with many flows to other fields of Mathematics as Differential Equations, Dynamical Systems, Algebra, and to Physics, Information Technology, Data Science. Flows in the opposite direction are extremely important as well. All these connections we call ”Related Fields”. The questions arising in artificial intelligence, large scale networks, computer architecture, when formulated as mathematical problems are often tightly related to the classical Markovian philosophy based on Kolmogorov equations, conditional probabilities etc.

For 3 decades the Journal provides interaction and links between modern trends and classical counterpart. We maintain the Journal as a vivid open scientific forum. We provide the quickest possible publication of papers falling into the described category. Tutorials and reviews on interdisciplinary connections are extremely welcome.

The international editorial board is composed of experts in probabil- ity theory. Otherwise, the journal ”Markov Processes and Related Fields” functions independently of any institution or external funds.

The journal is abstracted/indexed in:

Mathematical Reviews,

Science Citation Index-Expanded (SCIE) including the Web of Science, ISI Alerting Service, Current Contents/Physical,

Chemical and Earth Scienses (CC/PC&ES), Zentralblatt Math,

Russian Science Citation Index.

The journal ”Markov Processes and Related Fields” is a Crossref indexed journal, all articles are assigned a DOI.

Fields covered by the Journal

Amongst the main subjects covered by the Journal ”Markov Processes and Related Fields” are

1. Classical general and constructive theory of Markov processes, including classification criteria, convergence rates etc.
2. Random walks with boundaries, global and local properties of trajectories of random walks
3. Diffusion and jump processes
4. Random media
5. General theory of Markov and Gibbs random fields
6. General theory of processes with local interactions and infinite particle systems
7. Equilibrium and nonequilibrium statistical physics, quantum field theory
8. Scaling for large Markov processes, such as fluid models for finite-dimensional queueing systems, hydrodynamics for particle systems, diffusion approximation for queueing networks etc.
9. Evolution of random graphs, computer networks
10. Markovian aspects of neuronal networks, genetic algorithms, cellular automata
11. Telecommunications and computer networks
12. Controlled Markov processes and decision processes
13. Simulation of large Markov chains and Monte Carlo methods