Florence Forbes
- Statistics and Probability top 0.5%
- Statistical Methods and Inference 19
- Statistical Methods and Bayesian Inference 9
- Markov Chains and Monte Carlo Methods 8
-
- Medical Image Segmentation Techniques 11
- Ecological Modeling top 5%
- Artificial Intelligence top 2%
- Bayesian Methods and Mixture Models 34
- Genetics top 5%
-
- Advanced MRI Techniques and Applications 8
- Advanced Neuroimaging Techniques and Applications 8
-
- Speech and Audio Processing 7
- Co-authors
- Gilles CeleuxOlivier FrançoisD. M. TitteringtonCaroline RobertChibiao ChenÉric DurandNathalie PeyrardMichel Dojat
- Journals
- Journal of the American Statistical Association (2 papers)Bioinformatics (1 paper)IEEE Transactions on Pattern Analysis and Machine Intelligence (4 papers)
- Partner nations
- FranceAustraliaUnited States
In The Last Decade
Florence Forbes
87 papers receiving 2.6k citations
Hit Papers
Peers
Comparison fields: 5 of 173
- Statistics and Probability 538
- Computer Vision and Pattern Recognition 545
- Ecological Modeling 109
- Artificial Intelligence 782
- Genetics 568
Countries citing papers authored by Florence Forbes
This map shows the geographic impact of Florence Forbes'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 Florence Forbes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Florence Forbes more than expected).
Fields of papers citing papers by Florence Forbes
This network shows the impact of papers produced by Florence Forbes. 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 Florence Forbes. The network helps show where Florence Forbes may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Florence Forbes, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 63 | |
| 2 | 2023 | 1 | |
| 3 | 2022 | 1 | |
| 4 | 2022 | 2 | |
| 5 | 2020 | 12 | |
| 6 | 2020 | 0 | |
| 7 | 2019 | 10 | |
| 8 | 2019 | 3 | |
| 9 | 2019 | 1 | |
| 10 | 2019 | 3 | |
| 11 | 2016 | 1 | |
| 12 | 2016 | 62 | |
| 13 | 2015 | 21 | |
| 14 | 2014 | 9 | |
| 15 | 2012 | 12 | |
| 16 | 2009 | 52 | |
| 17 | 2008 | 11 | |
| 18 | 2005 | 1 | |
| 19 | 1999 | 7 | |
| 20 | 1998 | 3 |
About Florence Forbes
Florence Forbes is a scholar working on Statistics and Probability, Health Informatics and Artificial Intelligence, having authored 93 papers that have together received 2.7k indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (34 papers), Statistical Methods and Inference (19 papers), Medical Image Segmentation Techniques (11 papers), Statistical Methods and Bayesian Inference (9 papers), Advanced MRI Techniques and Applications (8 papers), Markov Chains and Monte Carlo Methods (8 papers), Advanced Neuroimaging Techniques and Applications (8 papers) and Speech and Audio Processing (7 papers). The work is most often cited by research in Statistics and Probability (538 citations), Computer Vision and Pattern Recognition (545 citations) and Ecological Modeling (109 citations). Florence Forbes has collaborated with scholars based in France, Australia and United States. Frequent co-authors include Gilles Celeux, Olivier François, D. M. Titterington, Caroline Robert, Chibiao Chen, Éric Durand, Nathalie Peyrard, Michel Dojat, Radu Horaud and Darren Wraith. Their work appears in journals such as Journal of the American Statistical Association, Bioinformatics and IEEE Transactions on Pattern Analysis and Machine Intelligence.
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.