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 https://www.mdpi.com/si/255192