Advance in Bayesian Inference and High-Dimensional Data Analysis
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 17
Special Issue Editors
Interests: bayesian statistics; computational statistics; high-dimensional inference; statsitical machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: bayesian statistics; computational statistics; mixture models; variable selection
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We are pleased to announce a Special Issue on “Advance in Bayesian Inference and High-Dimensional Data Analysis.” With the rapid development of computing facilities, Bayesian inference has emerged as a powerful framework for addressing complex statistical challenges, particularly in the context of high-dimensional data from diverse fields such as genomics, neuroscience, machine learning, and social science. As modern scientific and technological applications increasingly generate massive datasets, new Bayesian methods have been developed to enhance scalability, improve model selection, and ensure reliable inference.
This Special Issue will present a collection of the latest developments on the following topics, including but not limited to:
- Bayesian statistical modeling;
- Prior specification and elicitation;
- Bayesian hypothesis testing and variable selection;
- Markov Chain Monte Carlo (MCMC) and variational algorithms;
- Bayesian applications in different fields.
Prof. Dr. Min Wang
Prof. Dr. Liucang Wu
Guest Editors
Manuscript Submission Information
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Keywords
- Bayesian inference
- Bayesian computation
- prior elicitation
- Bayesian modeling
- variable selection
- Bayesian applications
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