G. Govaert
- Artificial Intelligence top 2%
- Statistics and Probability top 1%
- Signal Processing top 5%
- Computer Vision and Pattern Recognition top 10%
- Molecular Biology
- Co-authors
- Christophe BiernackiGilles CeleuxMohamed NadifChristophe AmbroiseHani HamdanDominique Meizel
- Topics
- Bayesian Methods and Mixture Models (11 papers)Advanced Clustering Algorithms Research (9 papers)Statistical Methods and Bayesian Inference (4 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligencePattern Recognition LettersComputational Statistics & Data Analysis
- Partner nations
- France
In The Last Decade
G. Govaert
13 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 149
- Artificial Intelligence 698
- Statistics and Probability 266
- Signal Processing 147
- Computer Vision and Pattern Recognition 139
- Molecular Biology 132
Countries citing papers authored by G. Govaert
This map shows the geographic impact of G. Govaert'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 G. Govaert with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites G. Govaert more than expected).
Fields of papers citing papers by G. Govaert
This network shows the impact of papers produced by G. Govaert. 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 G. Govaert. The network helps show where G. Govaert may publish in the future.
Co-authorship network of co-authors of G. Govaert
This figure shows the co-authorship network connecting the top 25 collaborators of G. Govaert. A scholar is included among the top collaborators of G. Govaert 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 G. Govaert. G. Govaert is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 23 | |
| 2 | 8 | |
| 3 | 62 | |
| 4 | 3 | |
| 5 | 12 | |
| 6 | 2 | |
| 7 | Process for a time-constrained radical new product development process (TR-NPD). A finite horizon discrete-time Markov nonstationary approach | 1 |
| 8 | Assessing a mixture model for clustering with the integrated completed likelihoodbreakdown → | 926 |
| 9 | 50 | |
| 10 | 1 | |
| 11 | 9 | |
| 12 | 10 | |
| 13 | 16 | |
| 14 | 2 |
About G. Govaert
G. Govaert is a scholar working on Statistics and Probability, Artificial Intelligence and Signal Processing, having authored 14 papers that have together received 1.1k indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (11 papers), Advanced Clustering Algorithms Research (9 papers) and Statistical Methods and Bayesian Inference (4 papers). The work is most often cited by research in Statistics and Probability (266 citations), Artificial Intelligence (698 citations) and Signal Processing (147 citations). G. Govaert has collaborated with scholars based in France. Frequent co-authors include Christophe Biernacki, Gilles Celeux, Mohamed Nadif, Christophe Ambroise, Hani Hamdan and Dominique Meizel. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition Letters and Computational Statistics & Data Analysis.
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.