Statistical Machine Learning and Bayesian Methods with Applications

Dear Colleagues,

We are pleased to introduce a Special Issue on “Statistical Machine Learning and Bayesian Methods with Applications”. The fields of statistical machine learning and Bayesian analysis are rapidly evolving, fuelled by theoretical and computational advances and an increasing demand for interpretable, trustworthy, and adaptable models in high-stakes applications. While machine learning has often been developed and tested on benchmark datasets, it has already shown its value in significant real-world applications and continues to hold great untapped potential across domains such as spatial statistics, survival analysis, healthcare, and environmental science.

This Special Issue seeks to highlight the synergy between modern Bayesian methods, including hierarchical, nonparametric, and deep probabilistic approaches, and statistical machine learning, with a particular focus on enhancing the transparency, robustness, and interpretability of predictive models. Contributions are encouraged that demonstrate both theoretical innovation and application-driven insights, providing proof-of-concept studies or real-world implementations with clear originality and significance.

Key topics of interest include, but are not limited to, the following:

• Statistical approaches in machine learning: Frameworks integrating statistical principles to improve generalization, stability, and uncertainty quantification;
• Interpretability and explainability: New methodologies to make predictive models, including black-box models, more transparent and understandable;
• Flexible and robust Bayesian modeling frameworks;
• Bayesian and probabilistic modeling for complex and structured data, including methods for challenging inference problems and real-world applications (e.g., spatial statistics, survival analysis, and geostatistical modelling…);
• Bayesian deep learning and probabilistic machine learning;
• Application-driven machine learning: Innovative applications with theoretical justification and demonstrable real-world impact;
• Applications emphasizing validation, interpretability, and practical significance.

Dr. Mélodie Monod
Dr. Serena Doria
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• Bayesian deep learning
• probabilistic machine learning
• statistical machine learning
• interpretability and explainability
• explainability
• spatial statistics
• survival analysis
• application-driven learning