Graphical Models
This special issue belongs to the section "Mathematical Analysis".
Special Issue Information
Dear Colleagues,
The aim of this Special Issue is to bring together cutting-edge graphical models research and its advancements. We seek to explore the various aspects of them, including their theoretical foundations, novel methodologies, and practical applications. With this collection of papers, we aim to foster a deeper understanding of the principles and techniques underlying graphical models, their computational aspects, and their applications in diverse domains, such as machine learning, data science, network analysis, and bioinformatics. We invite researchers to contribute original work, including theoretical investigations, algorithm development, case studies, and interdisciplinary applications, to this "Graphical Models" Special Issue. By showcasing a wide range of perspectives and approaches, this Special Issue will serve as a platform for knowledge exchange and collaboration.
Prof. Dhafer Malouche
Guest Editor
Manuscript Submission Information
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Keywords
- probabilistic graphical models
- Bayesian networks
- Markov random fields
- inference algorithms for graphical models
- learning graphical models
- causal graphical models
- graphical models in machine learning
- sparse graphical models
- graphical model selection
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Published Papers
Axioms - ISSN 2075-1680