Manuscript Topics

Bayesian machine learning (ML) methods have proven transformative over the last two decades, demonstrating significant potential across various applications. However, the full realisation of their potential to deliver societal and economic benefits is yet to be achieved. One key challenge lies in the insufficient development of methods and techniques for extracting value from real-world applications, particularly those dealing with complex data such as health-related and earth observation data. Complex data, characterised by noise, incompleteness, volume (big or small), heterogeneity, and lack of structured organisation, poses unique challenges. As the domain of data science expands, the demand for sophisticated methodologies becomes increasingly vital. This special issue is dedicated to showcasing the latest advancements in Bayesian machine learning techniques, providing a platform for the exchange of innovative ideas and methodologies. In particular, with the acceleration in the volume and complexity of data associated with real-world applications, we invite contributors to submit original and innovative approaches addressing the challenges of modelling this complex data. Manuscripts spanning a spectrum of applications, from theoretical developments to practical implementations, are welcomed. Emphasising the use of advanced Bayesian and probabilistic ML, the focus extends to tackling real-world challenges across diverse fields, including environmental science, health-related research, health economics, sport science, business systems and operations, and other relevant domains. The scope of this special issue is broad, encompassing but not confined to the following:

• Methodological enhancements
• Complex systems and optimization
• Graphical models, including Bayesian networks (BNs), Dynamic BNs, copula and vine models, chain event graph
• Decision support system
• Analytical modelling and simulation.
• Surrogate models, including Gaussian process, Physics-informed machine learning
• Applications of Bayesian machine learning in diverse fields.
• Markov Chain Monte Carlo and Variational Inference
• Uncertainty quantification & Sensitivity analysis
• Bayesian deep learning, including deep Gaussian process
• Modelling limited data using expert elicitation methods, including minimum-information graphical models
• Computational Algorithmic innovations
• Modelling innovations for big data and multi-modal data

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