Richard G. Everitt

1.7k total citations
19 papers, 823 citations indexed

About

Richard G. Everitt is a scholar working on Artificial Intelligence, Statistics and Probability and Molecular Biology. According to data from OpenAlex, Richard G. Everitt has authored 19 papers receiving a total of 823 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 8 papers in Statistics and Probability and 3 papers in Molecular Biology. Recurrent topics in Richard G. Everitt's work include Markov Chains and Monte Carlo Methods (7 papers), Bayesian Methods and Mixture Models (5 papers) and Target Tracking and Data Fusion in Sensor Networks (4 papers). Richard G. Everitt is often cited by papers focused on Markov Chains and Monte Carlo Methods (7 papers), Bayesian Methods and Mixture Models (5 papers) and Target Tracking and Data Fusion in Sensor Networks (4 papers). Richard G. Everitt collaborates with scholars based in United Kingdom, France and Mexico. Richard G. Everitt's co-authors include Daniel J. Wilson, Xavier Didelot, Tanya Golubchik, Tim Peto, Bernadette Young, Derrick W. Crook, A. Sarah Walker, M. Morgan, Pierre Alquier and Jack W. Finney and has published in prestigious journals such as Nature Communications, PLoS ONE and Journal of Clinical Microbiology.

In The Last Decade

Richard G. Everitt

19 papers receiving 807 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Richard G. Everitt United Kingdom 11 266 245 187 171 144 19 823
Theodore Kypraios United Kingdom 17 107 0.4× 199 0.8× 88 0.5× 117 0.7× 104 0.7× 61 908
Madeleine Cule United Kingdom 15 346 1.3× 687 2.8× 147 0.8× 93 0.5× 144 1.0× 35 1.5k
José Manuel Azcona Spain 15 189 0.7× 240 1.0× 74 0.4× 266 1.6× 19 0.1× 43 1.0k
Carla M. Carvalho Portugal 25 596 2.2× 266 1.1× 73 0.4× 327 1.9× 446 3.1× 42 2.2k
Philip D. O’Neill United Kingdom 23 104 0.4× 259 1.1× 35 0.2× 202 1.2× 249 1.7× 78 1.7k
Uwe Rösler Germany 21 239 0.9× 206 0.8× 54 0.3× 31 0.2× 42 0.3× 42 1.3k
Licheng Zhao China 18 215 0.8× 123 0.5× 159 0.9× 38 0.2× 9 0.1× 46 1.0k
Baharak Babouee Flury Switzerland 13 55 0.2× 96 0.4× 45 0.2× 35 0.2× 57 0.4× 31 726
Geoff Jones New Zealand 19 85 0.3× 251 1.0× 29 0.2× 88 0.5× 160 1.1× 67 1.2k
Susana Vinga Portugal 24 1.9k 7.1× 155 0.6× 50 0.3× 309 1.8× 33 0.2× 95 2.6k

Countries citing papers authored by Richard G. Everitt

Since Specialization
Citations

This map shows the geographic impact of Richard G. Everitt'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 Richard G. Everitt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Richard G. Everitt more than expected).

Fields of papers citing papers by Richard G. Everitt

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Richard G. Everitt. 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 Richard G. Everitt. The network helps show where Richard G. Everitt may publish in the future.

Co-authorship network of co-authors of Richard G. Everitt

This figure shows the co-authorship network connecting the top 25 collaborators of Richard G. Everitt. A scholar is included among the top collaborators of Richard G. Everitt 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 Richard G. Everitt. Richard G. Everitt is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Didelot, Xavier, et al.. (2023). Speeding up Inference of Homologous Recombination in Bacteria. Bayesian Analysis. 19(4). 1 indexed citations
2.
Boyd, Robin J., Ritabrata Dutta, Nicola Walker, et al.. (2022). Incorporating environmental variability in a spatially-explicit individual-based model of European sea bass✰. Ecological Modelling. 466. 109878–109878. 8 indexed citations
3.
Everitt, Richard G., et al.. (2020). Delayed Acceptance ABC-SMC. Journal of Computational and Graphical Statistics. 30(1). 55–66. 8 indexed citations
4.
Mansfield, Laura, Peer Nowack, Matthew Kasoar, et al.. (2020). Predicting global patterns of long-term climate change from short-term simulations using machine learning. npj Climate and Atmospheric Science. 3(1). 59 indexed citations
5.
Everitt, Richard G., et al.. (2019). Sequential Monte Carlo with transformations. Statistics and Computing. 30(3). 663–676. 5 indexed citations
6.
Everitt, Richard G.. (2017). Efficient importance sampling in low dimensions using affine arithmetic. Computational Statistics. 33(1). 1–29. 3 indexed citations
7.
Prangle, Dennis, Richard G. Everitt, & Theodore Kypraios. (2017). A rare event approach to high-dimensional approximate Bayesian computation. Statistics and Computing. 28(4). 819–834. 12 indexed citations
8.
Gordon, N Claire, James Price, Kevin Cole, et al.. (2014). Prediction of Staphylococcus aureus Antimicrobial Resistance by Whole-Genome Sequencing. Journal of Clinical Microbiology. 52(4). 1182–1191. 234 indexed citations
9.
Everitt, Richard G., Xavier Didelot, Elizabeth M. Batty, et al.. (2014). Mobile elements drive recombination hotspots in the core genome of Staphylococcus aureus. Nature Communications. 5(1). 3956–3956. 105 indexed citations
10.
Alquier, Pierre, et al.. (2014). Noisy Monte Carlo: convergence of Markov chains with approximate transition kernels. Statistics and Computing. 26(1-2). 29–47. 58 indexed citations
11.
Everitt, Richard G., Christophe Andrieu, & Manuel Davy. (2013). Online Bayesian Inference in Some Time-Frequency Representations of Non-Stationary Processes. IEEE Transactions on Signal Processing. 61(22). 5755–5766. 4 indexed citations
12.
Golubchik, Tanya, Elizabeth M. Batty, Ruth R. Miller, et al.. (2013). Within-Host Evolution of Staphylococcus aureus during Asymptomatic Carriage. PLoS ONE. 8(5). e61319–e61319. 164 indexed citations
13.
Everitt, Richard G.. (2012). Bayesian Parameter Estimation for Latent Markov Random Fields and Social Networks. Journal of Computational and Graphical Statistics. 21(4). 940–960. 45 indexed citations
14.
Didelot, Xavier, Richard G. Everitt, Adam M. Johansen, & Daniel J. Lawson. (2011). Likelihood-free estimation of model evidence. Bayesian Analysis. 6(1). 61 indexed citations
15.
Everitt, Richard G., et al.. (2008). A statistical approach to the problem of restoring damaged and contaminated images. Pattern Recognition. 42(1). 115–125. 3 indexed citations
16.
Maskell, Simon, Richard G. Everitt, R.W. Wright, & Mark Briers. (2005). Multi-target out-of-sequence data association: Tracking using graphical models. Information Fusion. 7(4). 434–447. 28 indexed citations
17.
Maskell, Simon, Richard G. Everitt, R.W. Wright, & Mark Briers. (2004). Multi-target out-of-sequence data association. CentAUR (University of Reading). 10 indexed citations
18.
Everitt, Richard G. & A. Marrs. (2004). Hypothesis management in situation assessment. CentAUR (University of Reading). 4. 4_1895–4_1903. 2 indexed citations
19.
Everitt, Richard G. & William T. Ziemba. (1979). Two-Period Stochastic Programs with Simple Recourse. Operations Research. 27(3). 485–502. 13 indexed citations

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.

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