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Advances in Bayesian Statistics

Special Issue Information

Dear Colleagues,

Bayesian statistics continues to revolutionize scientific research, offering a powerful framework for uncertainty quantification and decision making across diverse fields. This Special Issue seeks to showcase cutting-edge developments in Bayesian methodology alongside impactful real-world applications. We welcome contributions that advance computational techniques, theoretical foundations, and innovative modeling approaches, as well as studies demonstrating how Bayesian methods solve complex problems in domains such as medicine, ecology, economics, engineering, and machine learning. By bridging the gap between theory and practice, this collection aims to highlight the versatility and growing influence of Bayesian statistics in modern data analysis. We invite researchers to submit original work that pushes methodological boundaries or illustrates transformative applications, fostering discussion and collaboration across disciplines.

Dr. Weining Shen
Dr. Weixuan Zhu
Dr. Juan Miguel Marin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Entropy is an international peer-reviewed open access monthly 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 machine learning
  • empirical bayes
  • approximate Bayesian computation
  • Bayesian optimization
  • hierarchical models
  • variational inference
  • Bayesian model selection
  • Gaussian processes
  • posterior approximation
  • Bayesian nonparametrics

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