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A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces

Overview of attention for article published in Statistics and Computing, September 2006
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#13 of 276)
  • High Attention Score compared to outputs of the same age (89th percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

twitter
21 tweeters
patent
1 patent

Citations

dimensions_citation
362 Dimensions

Readers on

mendeley
319 Mendeley
citeulike
1 CiteULike
Title
A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces
Published in
Statistics and Computing, September 2006
DOI 10.1007/s11222-006-8769-1
Authors

Cajo J. F. Ter Braak

Twitter Demographics

The data shown below were collected from the profiles of 21 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 319 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 9 3%
Germany 3 <1%
United Kingdom 3 <1%
Canada 2 <1%
Sweden 1 <1%
Denmark 1 <1%
Brazil 1 <1%
China 1 <1%
Australia 1 <1%
Other 9 3%
Unknown 288 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 99 31%
Researcher 67 21%
Student > Master 44 14%
Professor > Associate Professor 23 7%
Student > Bachelor 17 5%
Other 69 22%
Readers by discipline Count As %
Engineering 52 16%
Agricultural and Biological Sciences 40 13%
Computer Science 36 11%
Unspecified 36 11%
Physics and Astronomy 32 10%
Other 123 39%

Attention Score in Context

This research output has an Altmetric Attention Score of 16. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 December 2017.
All research outputs
#933,093
of 13,167,099 outputs
Outputs from Statistics and Computing
#13
of 276 outputs
Outputs of similar age
#42,131
of 385,621 outputs
Outputs of similar age from Statistics and Computing
#1
of 6 outputs
Altmetric has tracked 13,167,099 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 276 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done particularly well, scoring higher than 95% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 385,621 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them