JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling
Impact in
- Ecology 1.3k
Classified as
- Authors
- Martyn Plummer
In The Last Decade
doi.org/w76018973 →Countries where authors are citing JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling
This map shows the geographic impact of JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. 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 JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling more than expected).
Fields of papers citing JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling
This network shows the impact of JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling.
About JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling
This paper, published in 2003, received 3.4k indexed citations . Written by Martyn Plummer covering the research area of Artificial Intelligence and Statistics and Probability. It is primarily cited by scholars working on Ecology (1.3k citations), Nature and Landscape Conservation (769 citations), Global and Planetary Change (569 citations), Statistics and Probability (453 citations) and Ecological Modeling (427 citations).
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.
This paper is also available at doi.org/w76018973.