Mark Beaumont: h-index, Total Citations, and Citation Map
Mark Beaumont's h-index is 63 (103 i10-index, 25,884+ total citations across 170+ publications) according to Google Scholar as of June 2026. Mark Beaumont is affiliated with Professor of Statistics, University of Bristol.
Mark Beaumont is a researcher affiliated with Professor of Statistics, University of Bristol, specializing in Bayesian Inference, Monte Carlo, Population Genetics. Their work has been cited 25,884 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Mark Beaumont's Citation Metrics
Bibliometric impact based on 170 indexed publications. Of these, 4 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 63
- i10-Index
- 103
- Total Citations
- 25,884
- Citing Countries
- 2
As of June 2026.
Mark Beaumont has an h-index of 63 and 25,884 total citations across 170 publications, with research cited by institutions in 2 countries.
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Approximate Bayesian computation in population genetics
20023,808
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Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Mark Beaumont's research
Mark Beaumont is a researcher in Bayesian Inference, Monte Carlo and Population Genetics at Professor of Statistics, University of Bristol. Their work has been cited 25,884 times across 170 publications (h-index 63), according to Google Scholar.
Their most-cited work, “Approximate Bayesian computation in population genetics” (2002), has accumulated 3,808 citations. Other influential works include “Evaluating loci for use in the genetic analysis of population structure” (1996) with 2,131 citations and “Approximate Bayesian computation in evolution and ecology” (2010) with 1,475 citations.
Citations of Mark Beaumont's research come primarily from United States and United Kingdom, reflecting international research impact. The interactive citation map above shows the full geographic distribution of the institutions citing this work.











