Bayesian Analysis

Aims & Scope

Bayesian Analysis is an electronic journal of the International Society for Bayesian Analysis.

It seeks to publish a wide range of articles that demonstrate or discuss Bayesian methods in some theoretical or applied context.

The journal welcomes submissions involving presentation of new computational and statistical methods; critical reviews and discussions of existing approaches; historical perspectives; description of important scientific or policy application areas; case studies; and methods for experimental design, data collection, data sharing, or data mining.

Evaluation of submissions is based on importance of content and effectiveness of communication.

Discussion papers are typically chosen by the Editor in Chief, or suggested by an Editor, among the regular submissions.

In addition, the Journal encourages individual authors to submit manuscripts for consideration as discussion papers.

Metrics & Ranking

Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Directory of Open Access Journals (DOAJ)
• Web of Science

Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Mathematics, designed to support cutting-edge academic discovery.

Licensing & Copyright

This journal operates under an Open Access model. Articles are freely accessible to the public immediately upon publication. The content is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing users to share and adapt the work with proper attribution.

Copyright remains with the author(s), and no permission is required for non-commercial use, provided the original source is cited.

Policy Links

This section provides access to essential policy documents, guidelines, and resources related to the journal’s publication and submission processes.

• Aims scope
• Homepage
• Oa statement
• Author instructions
• License terms
• Review url
• Board url
• Copyright url
• Apc url
• License

APC Details

The journal’s Article Processing Charge (APC) policies support open access publishing in Mathematics, ensuring accessibility and quality in research dissemination.

This journal does not charge a mandatory Article Processing Charge (APC). However, optional open access publication may incur fees based on the publisher’s policies.

Explore journals without APCs for alternative publishing options.

Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper)

  Citation: 3129

  Authors: Andrew

• Flexible paleoclimate age-depth models using an autoregressive gamma process

  Citation: 3073

  Authors: Maarten, J. Andrés

• Nested sampling for general Bayesian computation

  Citation: 1193

  Authors: John

• Rank-Normalization, Folding, and Localization: An Improved Rˆ for Assessing Convergence of MCMC (with Discussion)

  Citation: 941

  Authors: Aki, Andrew, Daniel, Bob, Paul-Christian

• Variational inference for Dirichlet process mixtures

  Citation: 891

  Authors: David M., Michael I.

• Deviance information criteria for missing data models

  Citation: 620

  Authors: G., F., C. P., D. M.

• A review of Bayesian variable selection methods: what, how and which

  Citation: 465

  Authors: R. B., M. J.

• The case for objective Bayesian analysis

  Citation: 433

  Authors: James

• Using Stacking to Average Bayesian Predictive Distributions (with Discussion)

  Citation: 403

  Authors: Yuling, Aki, Daniel, Andrew

• A comparison of Bayesian and likelihood-based methods for fitting multilevel models

  Citation: 402

  Authors: William J., David