Mark Briers
Impact in
- Modeling and Simulation top 5%
- COVID-19 epidemiological studies
- Artificial Intelligence top 5%
- Target Tracking and Data Fusion in Sensor Networks
- Gaussian Processes and Bayesian Inference
Papers in
-
- Target Tracking and Data Fusion in Sensor Networks 17
- Bayesian Modeling and Causal Inference 4
- Bayesian Methods and Mixture Models 3
- Gaussian Processes and Bayesian Inference 2
- Co-authors
- Simon R. Maskell (14 shared papers)Arnaud Doucet (6 shared papers)Luca Ferretti (3 shared papers)Chris Wymant (3 shared papers)Lucie Abeler- Dörner (3 shared papers)Michelle L. Kendall (3 shared papers)Christophe Fraser (3 shared papers)Chris C. Holmes (4 shared papers)
- Journals
- Nature (2 papers)Statistical Science (1 paper)Journal of Computational and Graphical Statistics (1 paper)Machine Vision and Applications (1 paper)Journal of the Royal Statistical Society Series C (Applied Statistics) (1 paper)
- Partner nations
- United KingdomCanadaAustralia
In The Last Decade
Mark Briers
27 papers receiving 901 citations
Mark Briers's Hit Papers
Peers
Comparison fields: 5 of 97
- Modeling and Simulation 114
- Artificial Intelligence 500
- Information Systems 196
- Statistics and Probability 67
- Computer Networks and Communications 142
Countries citing papers authored by Mark Briers
This map shows the geographic impact of Mark Briers'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 Mark Briers with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Briers more than expected).
Fields of papers citing papers by Mark Briers
This network shows the impact of papers produced by Mark Briers. 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 Mark Briers. The network helps show where Mark Briers may publish in the future.
Co-authors
The 25 scholars most cited alongside Mark Briers, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | The epidemiological impact of the NHS COVID-19 app Hit paper breakdown → | 2021 | 186 |
| 2 | 2009 | 177 | |
| 3 | 2006 | 116 | |
| 4 | 2006 | 95 | |
| 5 | 2020 | 66 | |
| 6 | 2005 | 41 | |
| 7 | 2003 | 35 | |
| 8 | 2023 | 32 | |
| 9 | 2005 | 29 | |
| 10 | 2005 | 25 | |
| 11 | 2005 | 19 | |
| 12 | 2004 | 18 | |
| 13 | 2005 | 14 | |
| 14 | 2022 | 13 | |
| 15 | 2004 | 12 | |
| 16 | 2012 | 12 | |
| 17 | Approximate Bayesian Computation for Source Term Estimation | 2009 | 11 |
| 18 | Multi-target out-of-sequence data association | 2004 | 9 |
| 19 | 2006 | 8 | |
| 20 | 2005 | 7 |
About Mark Briers
Mark Briers is a scholar working on Artificial Intelligence, Statistics and Probability, Modeling and Simulation, Computer Networks and Communications and Control and Systems Engineering, having authored 27 papers that have together received 949 indexed citations. Recurring topics across this work include Target Tracking and Data Fusion in Sensor Networks (17 papers), Distributed Sensor Networks and Detection Algorithms (5 papers), Fault Detection and Control Systems (5 papers), Bayesian Modeling and Causal Inference (4 papers), COVID-19 epidemiological studies (3 papers), Bayesian Methods and Mixture Models (3 papers), Gaussian Processes and Bayesian Inference (2 papers) and Advanced Vision and Imaging (2 papers). The work is most often cited by research in Modeling and Simulation (114 citations), Artificial Intelligence (500 citations), Information Systems (196 citations), Statistics and Probability (67 citations) and Computer Networks and Communications (142 citations). Mark Briers has collaborated with scholars based in United Kingdom, Canada and Australia. Frequent co-authors include Simon R. Maskell, Arnaud Doucet, Luca Ferretti, Chris Wymant, Lucie Abeler- Dörner, Michelle L. Kendall, Christophe Fraser, Chris C. Holmes, David Bonsall and Luke Heath Milsom. Their work appears in journals such as Nature, Statistical Science, Journal of Computational and Graphical Statistics, Machine Vision and Applications and Journal of the Royal Statistical Society Series C (Applied Statistics).
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.