Applications of Bayesian Methods in Statistical Analysis

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Analysis".

Deadline for manuscript submissions: 29 October 2024 | Viewed by 124

Special Issue Editors


E-Mail Website
Guest Editor
School of Mathematics and Statistics, Yunnan University, Kunming, China
Interests: Bayesian analysis; tensor regression; missing data

E-Mail Website
Guest Editor
School of Mathematics and Statistics, Yunnan University, Kunming, China
Interests: sampling statistics

Special Issue Information

Dear Colleagues,

The Bayesian method and its applications play an important role in many branches of statistics. This Special Issue, "Applications of Bayesian Methods in Statistical Analysis", aims to explore the cutting-edge applications of Bayesian statistics across various domains, including but not limited to machine learning, bioinformatics, environmental science, and social sciences. This Special Issue encompasses theoretical advancements, computational innovations, and practical applications of Bayesian methods.  This Special Issue aims to provide a comprehensive collection of articles that demonstrate the versatility and power of Bayesian methods in solving complex problems, enhancing decision-making processes, and contributing to the advancement of scientific research. It will seek to bridge the gap between theoretical developments in Bayesian statistics and their practical applications in various domains. Moreover, it will highlight the interdisciplinary nature of Bayesian statistics and encourage the exploration of new areas where these methods can be applied. Therefore, this Special Issue is designed to usefully supplement the existing literature on Bayesian statistics and its applications by filling gaps, highlighting new research directions, and showcasing practical implementations.

Dr. Yanqing Zhang
Prof. Dr. Puying Zhao
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. Axioms 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 2400 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 inference
  • variational Bayesian method
  • machine learning
  • Markov Chain Monte Carlo (MCMC) methods
  • applications in Bayesian methods
  • Bayesian prior
  • hierarchical model
  • parameter estimation
  • interval estimation
  • related topics about Bayesian method

Published Papers

This special issue is now open for submission.
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