Probability Theory and Stochastic Processes: Theory and Applications

A special issue of Axioms (ISSN 2075-1680).

Deadline for manuscript submissions: 31 December 2025 | Viewed by 347

Special Issue Editors


E-Mail Website
Guest Editor
1. ESS, Polytechnic of Porto, Rua Dr. António Bernardino de Almeida 400, 4200-072 Porto, Portugal
2. CEAUL—Centro de Estatística e Aplicações, Faculdade de Ciências, Universidade de Lisboa, 1150-082 Lisboa, Portugal
Interests: pooled samples for batched testing; meta-analysis; distribution mixtures; simulation

E-Mail Website
Guest Editor
1. Escola Superior de Tecnologia e Gestão, Instituto Politécnico de Leiria, Campus 2, Morro do Lena—Alto do Vieiro, Apartado 4163, 2411-901 Leiria, Portugal
2.CEAUL—Centro de Estatística e Aplicações, Faculdade de Ciências, Universidade de Lisboa, 1150-082 Lisboa, Portugal
Interests: distributions; mixtures; simulation; statistical inference; classification; data science; meta-analysis; epidemiology; group testing; dental age estimation; history and foundations of probability and statistics

E-Mail Website
Guest Editor
1. Escola Superior de Tecnologia e Gestão, Instituto Politécnico de Leiria, Campus 2, Morro do Lena—Alto do Vieiro, Apartado 4163, 2411-901 Leiria, Portugal
2. CEAUL—Centro de Estatística e Aplicações, Faculdade de Ciências, Universidade de Lisboa, 1150-082 Lisboa, Portugal
Interests: mixtures of distributions; meta-analysis; pooled samples for batched testing; machine learning models

Special Issue Information

Dear Colleagues,

Probability theory and stochastic processes are fundamental branches of mathematics with extensive applications in various scientific and engineering disciplines. Over the years, advancements in probability theory and stochastic processes have led to groundbreaking developments in areas such as machine learning, network analysis, and risk management, highlighting their importance in both theoretical and applied contexts. This Special Issue seeks to showcase the latest developments in this vibrant field, emphasizing both innovative theoretical contributions and practical applications that address real-world challenges.

The aim of this Special Issue is to bring together high-quality research that advances our understanding of probability theory and stochastic processes. This includes theoretical breakthroughs, novel methodologies, and significant applications that demonstrate the versatility and impact of these mathematical tools. This Special Issue aligns with the journal’s scope by fostering interdisciplinary connections and disseminating knowledge that bridges theoretical foundations and practical implementations.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Advances in stochastic modeling and simulation techniques;
  • Applications of stochastic processes in finance, insurance, and economics;
  • Statistical inference for stochastic processes;
  • Random walks, Markov chains, and their applications;
  • Stochastic differential equations and their numerical solutions;
  • Connections between probability theory and machine learning;
  • Network theory and random graph models;
  • Applications in biology, epidemiology, and environmental science;
  • Ergodic theory and long-term behavior of stochastic systems;
  • Computational approaches to high-dimensional probability problems.

We are looking forward to receiving your contributions.

Prof. Dr. João Paulo Martins
Prof. Dr. Rui Santos
Dr. Miguel Felgueiras
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Axioms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • probability theory
  • stochastic processes
  • random walks
  • Gauss–Markov processes
  • Markov chains
  • stochastic modeling
  • ergodic theory
  • numerical simulations
  • machine learning
  • neuronal modeling

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

28 pages, 464 KB  
Article
Analysis of a Retrial Queueing System Suitable for Modeling Operation of Ride-Hailing Platforms with the Dynamic Service Pricing
by Alexander Dudin, Sergei Dudin and Olga Dudina
Axioms 2025, 14(9), 714; https://doi.org/10.3390/axioms14090714 - 22 Sep 2025
Abstract
Effective operation of any service system requires optimal organization of the sharing of resources between the users (customers). To this end, it is necessary to elaborate on the mechanisms that allow for the mitigation of congestion, i.e., the accumulation of many users requiring [...] Read more.
Effective operation of any service system requires optimal organization of the sharing of resources between the users (customers). To this end, it is necessary to elaborate on the mechanisms that allow for the mitigation of congestion, i.e., the accumulation of many users requiring service. Due to the randomness of the user’s arrival process, congestions can occur even when an arrival rate is constant, e.g., the arrivals are described by the stationary Poisson process, which is assumed in the majority of existing papers. However, congestions can be more severe if the possibility of fluctuation of the instantaneous arrival rate exists. Such a possibility is an inherent feature of many systems and can be taken into account via the description of arrivals by the Markov arrival process (MAP). This makes the problem of congestion avoidance drastically more challenging. In many real-world systems, there exists the possibility of customer admission control via dynamic pricing. We propose a novel predictive mechanism of dynamic pricing. Decision moments coincide with the transition moments of the underlying process of the MAP. A customer may join or balk the system or postpone joining the system depending on the current cost. We illustrate the application of this mechanism in a multi-server retrial queueing model with dynamic service pricing. The behavior of the system is described by a multidimensional Markov chain with state-inhomogeneous transitions. Its stationary distribution is computed and may be used for solving the various problems of system revenue maximization via the choice of the proper pricing strategy. Full article
(This article belongs to the Special Issue Probability Theory and Stochastic Processes: Theory and Applications)
Show Figures

Figure 1

Back to TopTop