Bayesian Learning and Its Advanced Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".
Deadline for manuscript submissions: 25 June 2025 | Viewed by 1081
Special Issue Editors
Interests: Bayesian optimization; machine learning; multi-fidelity fusion; physics-enhanced machine learning; spatial–temporal field modeling for digital twins; machine learning techniques for engineering; electronic design automation
Interests: probabilistic machine learning; probabilistic graphical models; Bayesian deep learning; Bayesian nonparametrics; physics-informed machine learning; approximate inference; sparse learning; large-scale machine learning; kernel methods
Special Issue Information
Dear Colleagues,
This Special Issue focuses on the cutting-edge advancements and applications of Bayesian learning across various domains. Bayesian methods have gained significant traction in recent years due to their ability to quantify uncertainty, incorporate prior knowledge, and provide robust probabilistic frameworks for decision making. This Special Issue aims to showcase innovative research that pushes the boundaries of Bayesian learning, exploring its intersection with deep learning, physics-informed models, and large-scale data analysis. This Special Issue welcomes contributions that address theoretical developments, novel algorithms, and practical applications of Bayesian learning.
Topics of interest include, but are not limited to, the following:
- Bayesian optimization;
- Multi-fidelity fusion;
- Physics-enhanced machine learning;
- Spatial–temporal field modeling for digital twins;
- Probabilistic graphical models;
- Bayesian deep learning;
- Approximate inference techniques;
- Sparse learning methods.
We particularly encourage submissions that demonstrate the power of Bayesian approaches in solving complex real-world problems in engineering, electronic design automation, and other data-intensive fields.
This Special Issue seeks to bring together researchers and practitioners to share their latest findings, fostering cross-disciplinary collaborations and advancing the state of the art in Bayesian learning and its applications.
Dr. Wei W. Xing
Dr. Shandian Zhe
Guest Editors
Manuscript Submission Information
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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.
Keywords
- Bayesian learning
- variational inference
- Markov chain Monte Carlo (MCMC)
- Bayesian neural networks
- probabilistic graphical models
- Bayesian optimization
- Bayesian nonparametrics
- approximate Bayesian computation (ABC)
- Bayesian model selection
- Bayesian reinforcement learning
- uncertainty quantification
- multi-fidelity modeling
- physics-informed machine learning
- spatial–temporal modeling
- digital twins
- Bayesian methods for design and optimization
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