D1: Probability and Statistics
Section Information
The sub-section “Probability and Statistics” of Mathematics publishes original contributions that cover recent advances and reviews in the theory and application of probability and statistics.
We welcome papers dealing with all aspects of these disciplines, including the foundations of probability and statistics theory, probability theory and statistics on topological structures, combinatorial probability, stochastic geometry, distribution theory, limit theorems, stochastic processes, stochastic analysis, Markov processes, special processes, information-theoretic topics, decision theory, Bayesian problems, sampling theory, parametric and nonparametric inference, multivariate analysis, regression, sequential methods, inference from stochastic processes, survival analysis, data science, big data, and probabilistic methods and modeling in machine learning.
Special attention will be given to the theory and application of probability in the modeling of random phenomena in the natural sciences, social sciences, and technology. This section is also dedicated to the dissemination of methodological research and statistical techniques based on mathematical theory, analytical, algorithmic and experimental approaches, and computational methods.
Applications of stochastic models and statistical methods in diverse areas such as biology, medicine, epidemiology, economics, computer science, and statistical physics are also welcome.
Keywords
- asymptotic theory in statistical inference
- Bayesian methods (Bayesian analysis/Bayesian nonparametric/Bayesian networks)
- big data analysis (exploratory data/missing data analysis)
- bootstrapping and simulation-based inference
- causal inference
- combinatorial probability, geometric probability, and stochastic geometry
- computational methods in probability theory and stochastic processes
- dependence modeling
- distribution theory and new models for statistical inference
- efficiency evaluation using probabilistic development analysis
- high-dimensional statistical inference
- information geometry and information measures
- machine learning and artificial intelligence
- multi-source data fusion and inference/robust inference
- nonparametric methods in data science
- probability theory on algebraic and topological structures
- queuing theory and renewal theory
- regression models
- reliability theory
- statistical mechanics/statistical physics/statistical thermodynamics
- statistical tests
- stochastic analysis, stochastic integrals, and stochastic calculus of variations
- stochastic differential equations and random operators
- stochastic processes, modeling, and applications
- theory and methods of unstructured data measurement
- time series analysis and forecasting
- uncertainty quantification
Published Papers
Mathematics - ISSN 2227-7390