Bayesian Analysis seeks to publish a wide range of articles that demonstrate or discuss Bayesian methods in some theoretical or applied context.
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.
Bayesian Analysis is indexed in ISI Science Citation Index Expanded, MathSciNet, Scopus, and zbMATH, among others. In addition to the metrics listed below, Bayesian Analysis has a Journal Citation Indicator of 1.16 (top quartile in its fields), an Article Influence Score (2020) of 2.860, SCImago SJR (2020) of 2.685, and a SCImago (2020) Ranking of 16/257 (Statistics & Probability).
Author or publication fees are not required. Voluntary fees or donations to the Open Access Fund are accepted. Expenses not covered by voluntary payments are paid for by the International Society of Bayesian Analysis (ISBA) as a service to the community.
Bayesian Analysis has chosen to apply the Creative Commons Attribution 4.0 International License (CCAL) to all articles we publish in this journal. Under the CCAL, authors retain ownership of the copyright for their article, but authors allow anyone to download, reuse, reprint, modify, distribute, and/or copy articles in Bayesian Analysis, so long as the original authors and source are credited. This broad license was developed to facilitate open access to, and free use of, original works of all types. Applying this standard license to your work will ensure your right to make your work freely and openly available.
Bayesian Analysis
Print ISSN: 1936-0975Online ISSN: 1931-6690
Current: Mar 2025 : Volume 20 Issue 1
Coverage: 2006 - current
Frequency: Quarterly
Access: All content is open.
METRICS
Impact Factor: 3.728
Five-Year Impact Factor: 3.596
Journal Citation Reports® Ranking: #14 of 125 in Statistics & Probability; #17 of 108 in Interdisciplinary Applications of Mathematics