Paul Fearnhead
- Artificial Intelligence top 0.5%
- Statistics and Probability top 0.2%
- Molecular Biology top 10%
- Genetics top 2%
- Food Science top 1%
- Co-authors
- Idris A. EckleyRebecca KillickGil McVeanPhilip AwadallaDennis PranglePeter DonnellyPeter CliffordGareth O. Roberts
- Topics
- Bayesian Methods and Mixture Models (38 papers)Statistical Methods and Inference (24 papers)Markov Chains and Monte Carlo Methods (18 papers)
- Partner nations
- United KingdomUnited StatesFrance
In The Last Decade
Paul Fearnhead
120 papers receiving 6.1k citations
Hit Papers
Peers
Comparison fields: 5 of 202
- Artificial Intelligence 1.8k
- Statistics and Probability 1.5k
- Molecular Biology 993
- Genetics 971
- Food Science 656
Countries citing papers authored by Paul Fearnhead
This map shows the geographic impact of Paul Fearnhead'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 Paul Fearnhead with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Paul Fearnhead more than expected).
Fields of papers citing papers by Paul Fearnhead
This network shows the impact of papers produced by Paul Fearnhead. 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 Paul Fearnhead. The network helps show where Paul Fearnhead may publish in the future.
Co-authorship network of co-authors of Paul Fearnhead
This figure shows the co-authorship network connecting the top 25 collaborators of Paul Fearnhead. A scholar is included among the top collaborators of Paul Fearnhead 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 Paul Fearnhead. Paul Fearnhead is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 3 | |
| 5 | 7 | |
| 6 | 1 | |
| 7 | 19 | |
| 8 | Large-Scale Stochastic Sampling from the Probability Simplex | 2 |
| 9 | 89 | |
| 10 | 37 | |
| 11 | 303 | |
| 12 | 26 | |
| 13 | Semi-automatic Approximate Bayesian Computation | 9 |
| 14 | 15 | |
| 15 | 133 | |
| 16 | 4 | |
| 17 | 115 | |
| 18 | 14 | |
| 19 | 12 | |
| 20 | 189 |
About Paul Fearnhead
Paul Fearnhead is a scholar working on Statistics and Probability, Artificial Intelligence and Statistics, Probability and Uncertainty, having authored 124 papers that have together received 6.4k indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (38 papers), Statistical Methods and Inference (24 papers) and Markov Chains and Monte Carlo Methods (18 papers). The work is most often cited by research in Statistics and Probability (1.5k citations), Artificial Intelligence (1.8k citations) and Statistics, Probability and Uncertainty (321 citations). Paul Fearnhead has collaborated with scholars based in United Kingdom, United States and France. Frequent co-authors include Idris A. Eckley, Rebecca Killick, Gil McVean, Philip Awadalla, Dennis Prangle, Peter Donnelly, Peter Clifford, Gareth O. Roberts, Zhen Liu and Guillem Rigaill. Their work appears in journals such as Journal of the American Statistical Association, Bioinformatics and PLoS ONE.
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.