Bayesian Analysis

726 papers and 23.0k indexed citations
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About

The 726 papers published in Bayesian Analysis in the last decades have received a total of 23.0k indexed citations. Papers published in Bayesian Analysis usually cover Statistics and Probability (485 papers), Artificial Intelligence (397 papers) and Management Science and Operations Research (71 papers) specifically the topics of Statistical Methods and Inference (303 papers), Bayesian Methods and Mixture Models (295 papers) and Statistical Methods and Bayesian Inference (281 papers). The most active scholars publishing in Bayesian Analysis are Andrew Gelman, J. Andrés Christen, Maarten Blaauw, John Skilling, James O. Berger, Michael I. Jordan, David M. Blei, Stephen E. Fienberg, Robert B. O’Hara and Mikko J. Sillanpää.

In The Last Decade

Bayesian Analysis

625 papers receiving 21.6k citations

Fields of papers published in Bayesian Analysis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Bayesian Analysis. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in Bayesian Analysis.

Countries where authors publish in Bayesian Analysis

Since Specialization
Citations

This map shows the geographic impact of research published in Bayesian Analysis. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by papers published in Bayesian Analysis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bayesian Analysis more than expected).

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar’s output or impact.

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