Applied Probability and Statistics: Theory, Methods, and Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".
Deadline for manuscript submissions: 31 October 2026 | Viewed by 171
Special Issue Editor
Interests: applied probability; mathematical statistics; stochastic processes; statistical modeling; high-dimensional data analysis; penalized and regularized methods; statistical learning; uncertainty quantification; resampling and bootstrap methods; Bayesian and frequentist inference; causal and predictive modeling; applied mathematics
Special Issue Information
Dear Colleagues,
Applied probability and statistics constitute a fundamental pillar of modern mathematical research, with growing relevance across scientific and engineering disciplines. The increasing availability of complex, high-dimensional, and structured data has stimulated the development of new probabilistic models and statistical methodologies that extend classical frameworks and address contemporary analytical challenges.
This Special Issue aims to provide a forum for recent advances in applied probability and statistics; it will emphasize both theoretical developments and methodological innovations with practical relevance. We welcome contributions that propose new probabilistic models, statistical inference techniques, or computational methods, as well as studies that apply established approaches in novel or challenging contexts.
Topics of interest include, but are not limited to, the following: stochastic processes, probabilistic modeling, statistical learning, high-dimensional inference, regularization and variable selection, resampling and bootstrap methods, uncertainty quantification, Bayesian and frequentist inference, and modern approaches to prediction and modeling. Contributions addressing complex data structures such as dependence, heterogeneity, or non-standard sampling schemes are particularly encouraged.
Applications may arise in diverse fields, including, but not limited to, data science, engineering, economics, environmental sciences, public health, and decision-support systems. By bringing together methodological and applied contributions, this Special Issue seeks to highlight the versatility of probabilistic and statistical tools for addressing complex real-world problems while maintaining strong mathematical foundations.
We invite researchers to submit original research articles and comprehensive reviews that will advance the theory and application of probability and statistics in contemporary scientific research.
Prof. Dr. Fernando Rojas
Guest Editor
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 250 words) can be sent to the Editorial Office for assessment.
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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
- applied probability
- mathematical statistics
- stochastic processes
- statistical modeling
- high-dimensional data
- statistical learning
- uncertainty quantification
- resampling methods
- Bayesian and frequentist inference
- applied mathematics
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