Integration of Statistical Computing and Artificial Intelligence: Theoretical Methods and Empirical Application

Dear Colleagues，

With the advent of the big data era, the demand for processing and analyzing massive amounts of data has led to continuous innovation in statistical calculation methods. The rise of artificial intelligence has provided powerful tools for statistical calculation processes such as data mining and model construction. The combination of the two has shown great potential for application in many fields, giving rise to numerous cutting-edge research results. This Special Issue aims to focus on showcasing the latest developments in this interdisciplinary field

Scope and Topics of Interest:

(1) The Application of Statistical Computing Methods in Artificial Intelligence

This Special Issue will explore the specific applications of traditional statistical computing techniques, such as Bayesian statistics and Monte Carlo methods, in optimizing deep learning models, designing neural network structures, and improving reinforcement learning algorithms. Moreover, it will consider the  statistical learning theory for artificial intelligence, for example, the  regularization techniques, and dimension reduction. It will analyze how they improve the performance, accuracy, and stability of artificial intelligence models, as well as their advantages in solving practical problems.

(2) Artificial Intelligence Driven Innovation in Statistical Computing

This Special Issue will explore how artificial intelligence algorithms, especially machine learning algorithms, can drive innovation in statistical computing methods, such as new strategies for feature selection and model selection based on artificial intelligence, as well as adaptive statistical modeling methods. It will investigate how these innovations can break through the bottlenecks of traditional statistical computing and more efficiently handle complex data structures and large-scale datasets.

(3) Application Research of Interdisciplinary Integration

This Special Issue will also integrate the application examples of statistical computing and artificial intelligence in different disciplinary fields, such as financial risk prediction, meteorological and climate modeling, etc., to demonstrate how to use interdisciplinary cooperation and the synergistic effect of statistical computing and artificial intelligence to solve complex problems that are difficult to overcome by traditional methods, bringing new ideas and solutions to the development of related fields.

Dr. Yuping Song
Prof. Dr. Hanchao Wang
Guest Editors

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• statistical computing
• artificial intelligence
• Bayesian statistics
• Monte Carlo
• deep learning
• neural network
• regularization technique
• model parameter tuning
• feature selection
• model selection
• adaptive modeling
• machine learning
• financial risk prediction
• price trend prediction
• meteorological and climate modeling