Christophe Biernacki
- Statistics and Probability top 0.5%
- Statistical Methods and Bayesian Inference 11
- Statistical Methods and Inference 8
- Advanced Statistical Methods and Models 4
- Artificial Intelligence top 1%
- Bayesian Methods and Mixture Models 35
- Advanced Clustering Algorithms Research 14
- Bayesian Modeling and Causal Inference 8
- Machine Learning and Data Classification 3
- Signal Processing top 2%
- Data Management and Algorithms 5
- Transportation top 10%
- Co-authors
- Gilles CeleuxG. GovaertGérard GovaertJulien JacquesIsabelle ThomasPierre FrankhauserMatthieu MarbacStéphane Chrétien
In The Last Decade
Christophe Biernacki
50 papers receiving 2.1k citations
Hit Papers
Peers
Comparison fields: 5 of 172
- Statistics and Probability 562
- Artificial Intelligence 1.3k
- Signal Processing 264
- Computational Mathematics 7
- Transportation 59
Countries citing papers authored by Christophe Biernacki
This map shows the geographic impact of Christophe Biernacki'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 Christophe Biernacki with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christophe Biernacki more than expected).
Fields of papers citing papers by Christophe Biernacki
This network shows the impact of papers produced by Christophe Biernacki. 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 Christophe Biernacki. The network helps show where Christophe Biernacki may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Christophe Biernacki, 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 | 3 | |
| 2 | 2023 | 4 | |
| 3 | 2022 | 2 | |
| 4 | 2020 | 12 | |
| 5 | 2020 | 5 | |
| 6 | 2019 | 17 | |
| 7 | 2015 | 4 | |
| 8 | 2015 | 25 | |
| 9 | 2013 | 45 | |
| 10 | A predictive deviance criterion for selecting a generative model in semi-supervised classification | 2012 | 1 |
| 11 | Modèles génératifs de rangs relatifs à un algorithme de tri par insertion | 2010 | 1 |
| 12 | A generative model for rank data based on sorting algorithm | 2009 | 4 |
| 13 | Pourquoi les modeles de melange pour la classification | 2009 | 0 |
| 14 | 2007 | 5 | |
| 15 | 2005 | 4 | |
| 16 | 2004 | 16 | |
| 17 | 2002 | 11 | |
| 18 | Strategies for Getting the Highest Likelihood in Mixture Models | 2001 | 5 |
| 19 | 1999 | 95 | |
| 20 | Assessing a Mixture Model for Clustering with the Integrated Classification Likelihood | 1998 | 66 |
About Christophe Biernacki
Christophe Biernacki is a scholar working on Statistics and Probability, Artificial Intelligence and Signal Processing, having authored 52 papers that have together received 2.2k indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (35 papers), Advanced Clustering Algorithms Research (14 papers), Statistical Methods and Bayesian Inference (11 papers), Bayesian Modeling and Causal Inference (8 papers), Statistical Methods and Inference (8 papers), Data Management and Algorithms (5 papers), Advanced Statistical Methods and Models (4 papers) and Machine Learning and Data Classification (3 papers). The work is most often cited by research in Statistics and Probability (562 citations), Artificial Intelligence (1.3k citations) and Signal Processing (264 citations). Christophe Biernacki has collaborated with scholars based in France, Canada and Belgium. Frequent co-authors include Gilles Celeux, G. Govaert, Gérard Govaert, Julien Jacques, Isabelle Thomas, Pierre Frankhauser, Matthieu Marbac, Stéphane Chrétien, Emil Eirola and Amaury Lendasse. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Biometrics and Monthly Notices of the Royal Astronomical Society.
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.