Mathieu Gerber
Impact in
- Statistics and Probability top 5%
- Markov Chains and Monte Carlo Methods
- Statistical Methods and Inference
- Statistical Methods and Bayesian Inference
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- Probabilistic and Robust Engineering Design
Papers in ⓘ
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- Markov Chains and Monte Carlo Methods 5
- Statistical Methods and Inference 4
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- Bayesian Methods and Mixture Models 3
- Co-authors
- Christian P. Robert (3 shared papers)Pierre Jacob (3 shared papers)Christophe Espanet (3 shared papers)Luke Bornn (1 shared paper)Nicolás Chopin (2 shared papers)Daniel Depernet (2 shared papers)Nick Whiteley (1 shared paper)Pierre Alquier (1 shared paper)
- Journals
- Journal of the Royal Statistical Society Series B (Statistical Methodology) (1 paper)Bernoulli (1 paper)Information and Inference A Journal of the IMA (1 paper)SIAM Journal on Numerical Analysis (1 paper)Journal of Global Optimization (1 paper)
- Partner nations
- FranceUnited KingdomUnited States
In The Last Decade
Mathieu Gerber
12 papers receiving 119 citations
Peers
Comparison fields: 5 of 53
- Statistics and Probability 64
- Statistics, Probability and Uncertainty 19
- Artificial Intelligence 47
- Applied Mathematics 13
- Numerical Analysis 6
Countries citing papers authored by Mathieu Gerber
This map shows the geographic impact of Mathieu Gerber'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 Mathieu Gerber with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mathieu Gerber more than expected).
Fields of papers citing papers by Mathieu Gerber
This network shows the impact of papers produced by Mathieu Gerber. 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 Mathieu Gerber. The network helps show where Mathieu Gerber may publish in the future.
Co-authors
The 12 scholars most cited alongside Mathieu Gerber, 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 | 2019 | 62 | |
| 2 | 2019 | 29 | |
| 3 | 2015 | 8 | |
| 4 | 2015 | 6 | |
| 5 | 2016 | 3 | |
| 6 | 2016 | 3 | |
| 7 | 2017 | 3 | |
| 8 | 2017 | 2 | |
| 9 | 2017 | 2 | |
| 10 | 2024 | 1 | |
| 11 | 2023 | 1 | |
| 12 | 2019 | 1 |
About Mathieu Gerber
Mathieu Gerber is a scholar working on Statistics and Probability, Artificial Intelligence, Statistics, Probability and Uncertainty, Numerical Analysis and Control and Systems Engineering, having authored 12 papers that have together received 121 indexed citations. Recurring topics across this work include Markov Chains and Monte Carlo Methods (5 papers), Statistical Methods and Inference (4 papers), Probabilistic and Robust Engineering Design (3 papers), Electric Motor Design and Analysis (3 papers), Mathematical Approximation and Integration (3 papers), Magnetic Properties and Applications (3 papers), Bayesian Methods and Mixture Models (3 papers) and Stochastic processes and statistical mechanics (2 papers). The work is most often cited by research in Statistics and Probability (64 citations), Statistics, Probability and Uncertainty (19 citations), Artificial Intelligence (47 citations), Applied Mathematics (13 citations) and Numerical Analysis (6 citations). Mathieu Gerber has collaborated with scholars based in France, United Kingdom and United States. Frequent co-authors include Christian P. Robert, Pierre Jacob, Christophe Espanet, Luke Bornn, Nicolás Chopin, Daniel Depernet, Luke Bornn, Nick Whiteley, Pierre Alquier and Frédéric Dubas. Their work appears in journals such as Journal of the Royal Statistical Society Series B (Statistical Methodology), Bernoulli, Information and Inference A Journal of the IMA, SIAM Journal on Numerical Analysis and Journal of Global Optimization.
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.