Statistics & Probability (STP)

Section Information

Statistics & Probability focuses on the development and application of methods for analyzing randomness, uncertainty, and variability in data. It provides the theoretical and practical foundation for data-driven decision making across science, engineering, medicine, business, and social sciences.

Modern research in this field includes probabilistic modeling, statistical inference, stochastic processes, data analysis, machine learning, experimental design, computational statistics, and high-dimensional methods. Advances in simulation, Bayesian approaches, nonparametric techniques, and applied probability continue to expand the scope of statistical science.

This section publishes theoretical developments, applied studies, methodological research, simulations, reviews, and case analyses addressing statistical modeling, probability theory, data interpretation, uncertainty quantification, and innovative statistical tools.

Scope

Probability Theory and Stochastic Processes
- Random variables, distributions, and limit theorems
- Markov chains, martingales, and stochastic calculus
- Point processes, diffusion processes, and random walks
- Applications in physics, finance, biology, and engineering
Statistical Inference and Estimation
- Classical and Bayesian inference frameworks
- Hypothesis testing, interval estimation, and decision theory
- Nonparametric and semiparametric methods
- Robust estimation and resampling approaches
Regression, Modeling, and Machine Learning
- Linear, nonlinear, and generalized linear models
- High-dimensional modeling and regularization techniques
- Supervised and unsupervised learning algorithms
- Predictive modeling, validation, and performance assessment
Design of Experiments and Sampling
- Experimental design theory and optimal design
- Sampling strategies, survey methods, and bias control
- Randomization, blocking, and factorial experiments
- Applications to scientific research and quality improvement
Time Series Analysis and Forecasting
- Autoregressive, moving average, and state-space models
- Seasonality, trend analysis, and spectral methods
- Stochastic volatility, long-memory processes, and nonlinear dynamics
- Applications in economics, climate science, and engineering
Computational and Applied Statistics
- Monte Carlo simulation, MCMC, and numerical optimization
- Bootstrapping, permutation tests, and computational inference
- Statistical computing, software development, and reproducibility
- Big data analytics, parallel computing, and scalable algorithms
Multivariate Analysis and Dimension Reduction
- Principal component analysis, factor analysis, and clustering
- Classification, discriminant analysis, and latent variable models
- Manifold learning and high-dimensional inference
- Applications to genomics, finance, and image analysis
Applied Probability and Risk Modeling
- Queuing theory, reliability, and survival analysis
- Risk, uncertainty modeling, and decision analysis
- Stochastic optimization and operations research
- Applications in insurance, healthcare, and environmental studies