Computational Statistics with Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 61
Special Issue Editor
Special Issue Information
Dear Colleagues,
In recent years, the growing complexity of data and the increasing demand for accurate, efficient, and scalable analysis methods have significantly elevated the importance of computational statistics, also known as statistical computing. This field lies at the intersection of statistical theory, numerical computation, and data-driven applications, enabling the extraction of meaningful insights from complex and high-dimensional datasets.
Computational statistics encompasses a wide range of computational techniques, including Monte Carlo methods, numerical optimization, Bayesian computation, machine learning, and high-performance statistical computing. These tools play a critical role in modern statistical analysis and are widely used in applications such as biostatistics, finance, environmental science, image and signal processing, social sciences, and engineering.
This Special Issue aims to bring together original research articles and comprehensive reviews that highlight recent advances in computational statistics and their practical applications.
Dr. Minwoo Kim
Guest Editor
Manuscript Submission Information
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Keywords
- statistical algorithms
- big data
- optimization
- bayesian computing
- machine learning
- artificial intelligence
- Monte Carlo methods
- computational statistics
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