Richard A. Redner
- Artificial Intelligence top 1%
- Computer Vision and Pattern Recognition top 2%
- Statistics and Probability top 0.5%
- Signal Processing top 2%
- Media Technology top 2%
- Co-authors
- Homer F. WalkerSamuel P. UseltonAlbert C. ReynoldsKevin A. O’NeilZ. SchmidtD. R. DotyDean S. OliverL. G. Thompson
- Topics
- Bayesian Methods and Mixture Models (8 papers)Reservoir Engineering and Simulation Methods (8 papers)Statistical Methods and Inference (6 papers)
- Cited by
- Statistics and ProbabilityComputer Graphics and Computer-Aided DesignArtificial Intelligence
- Partner nations
- United StatesBrazilNetherlands
In The Last Decade
Richard A. Redner
24 papers receiving 2.2k citations
Hit Papers
Peers
Comparison fields: 5 of 154
- Artificial Intelligence 1.1k
- Computer Vision and Pattern Recognition 599
- Statistics and Probability 582
- Signal Processing 320
- Media Technology 181
Countries citing papers authored by Richard A. Redner
This map shows the geographic impact of Richard A. Redner's research. 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 Richard A. Redner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Richard A. Redner more than expected).
Fields of papers citing papers by Richard A. Redner
This network shows the impact of papers produced by Richard A. Redner. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Richard A. Redner. The network helps show where Richard A. Redner may publish in the future.
Co-authorship network of co-authors of Richard A. Redner
This figure shows the co-authorship network connecting the top 25 collaborators of Richard A. Redner. A scholar is included among the top collaborators of Richard A. Redner based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Richard A. Redner. Richard A. Redner is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 7 | |
| 2 | 3 | |
| 3 | 2 | |
| 4 | 13 | |
| 5 | 40 | |
| 6 | 5 | |
| 7 | 10 | |
| 8 | 9 | |
| 9 | 8 | |
| 10 | 23 | |
| 11 | 1 | |
| 12 | 16 | |
| 13 | 25 | |
| 14 | 16 | |
| 15 | 123 | |
| 16 | 17 | |
| 17 | Mixture Densities, Maximum Likelihood and the EM Algorithmbreakdown → | 1853 |
| 18 | The Akaike information criterion and its application to mixture proportion estimation | 3 |
| 19 | Mixture densities, maximum likelihood, and the EM algorithm | 3 |
| 20 | 115 |
About Richard A. Redner
Richard A. Redner is a scholar working on Statistics and Probability, Computer Graphics and Computer-Aided Design and Ocean Engineering, having authored 25 papers that have together received 2.4k indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (8 papers), Reservoir Engineering and Simulation Methods (8 papers) and Statistical Methods and Inference (6 papers). The work is most often cited by research in Statistics and Probability (582 citations), Computer Graphics and Computer-Aided Design (138 citations) and Artificial Intelligence (1.1k citations). Richard A. Redner has collaborated with scholars based in United States, Brazil and Netherlands. Frequent co-authors include Homer F. Walker, Samuel P. Uselton, Albert C. Reynolds, Kevin A. O’Neil, Z. Schmidt, D. R. Doty, Dean S. Oliver, L. G. Thompson, James C. Bezdek and Richard J. Hathaway. Their work appears in journals such as ACM Transactions on Graphics, The Annals of Statistics and SIAM Review.
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.