Probabilistic & Statistical Mathematics
A section of AppliedMath (ISSN 2673-9909).
Section Information
This Section focuses on theoretical innovations, methodological development, and interdisciplinary applications of probabilistic and statistical mathematics. Its core concerns include the construction of probability models (e.g., Bayesian models, Markov processes, stochastic processes), statistical inference and learning algorithms (e.g., parameter estimation, nonparametric statistics, statistical theories in machine learning), risk analysis, and uncertainty quantification. It particularly welcomes studies exploring the integration of this field with application scenarios such as financial engineering, biostatistics, environmental science, and data science. As a core Section of the AppliedMath journal, it aims to provide a platform for interdisciplinary research that combines probabilistic and statistical mathematics with practical problems, complementing the journal’s coverage in the direction of "uncertainty modeling and analysis" and promoting the transformation of theoretical methods into application scenarios.
