Theory and Applications of Probability Theory and Stochastic Analysis
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 5
Special Issue Editor
Special Issue Information
Dear Colleagues,
Probability, stochastic processes, and statistics form the mathematical backbone for understanding uncertainty, modeling dynamic random systems, and transforming data into actionable knowledge across science, engineering, and social sciences. In an era of unprecedented data complexity—characterized by high dimensionality, non-stationarity, and cross-disciplinary fusion—these fields face critical challenges, such as refining stochastic process frameworks to capture real-world temporal/spatial dependencies, advancing statistical inference under model misspecification, and integrating probabilistic methods with emerging technologies like machine learning and causal analysis.
This Special Issue aims to showcase cutting-edge research that bridges theoretical rigor and practical impact. We welcome contributions spanning foundational advances (e.g., novel probability bounds, robust stochastic process models) and interdisciplinary applications (e.g., biostatistical modeling of disease spread, stochastic optimization in finance, probabilistic AI for decision-making). By fostering dialogue between theorists and practitioners, the Issue seeks to address modern uncertainties, from climate variability to algorithmic bias, through innovative methodological developments.
Prof. Dr. Junping Li
Guest Editor
Manuscript Submission Information
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Keywords
- probability theory
- stochastic processes
- statistical inference
- high-dimensional data analysis
- causal inference
- Bayesian Methods
- spatiotemporal modeling
- uncertainty quantification
- machine learning integration
- interdisciplinary data science
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