Research on Stochastic Analysis and Applied Statistics
A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Analysis".
Deadline for manuscript submissions: 31 October 2024 | Viewed by 569
Special Issue Editors
Interests: data science; applied statistics; computational neuroscience; stochastic processes
Interests: nonparametric classification/discrimination; statistical pattern recognition; cluster analysis; data mining; biostatistics; design, and analysis of experiments; statistical computing and applied statistics
Special Issue Information
Dear Colleagues,
This Special Issue delves into the interplay between stochastic analysis, applied statistics, and the transformative impacts of machine learning and deep learning. With a comprehensive scope that spans mathematical modeling, statistics, and data science, this initiative seeks to bridge the gap between theoretical understanding and practical applications. The issue covers topics such as stochastic processes and applied probability, which are pivotal in various disciplines for modeling unpredictability. It highlights the importance of statistical modeling and data analysis in navigating complex datasets and extracting deep insights, thus showcasing their relevance in fields like healthcare analytics and financial forecasting.
Moreover, the issue emphasizes the role of computational statistics and the algorithmic advancements tailored to address the challenges of handling large volumes of data. These efforts include the development of the advanced statistical software and tools essential for modern data analysis. The fusion of machine learning and deep learning with traditional statistical methods represents a key theme, illustrating how these innovative technologies can improve pattern recognition and predictive modeling in large and complex datasets.
Targeting a wide readership, this Special Issue fosters interdisciplinary dialogue among professionals, academics, and students. It serves as a valuable resource for those interested in the theory behind stochastic processes and the practical application of statistical methods to real-world problems. By showcasing the latest research findings and methodological innovations, this Issue aims to inspire further research and practical applications in the ever-evolving fields of statistics, data science, and mathematical modeling, making a significant contribution to their advancement.
Dr. Shusen Pu
Prof. Dr. Subhash Bagui
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
- statistical modelling
- applied probability
- data analysis
- computational statistics
- stochastic processes
- machine learning
- deep learning
