| 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 |
X Demographics
The data shown below were collected from the profiles of 18 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 22% |
| Germany | 2 | 11% |
| Argentina | 1 | 6% |
| Korea, Republic of | 1 | 6% |
| India | 1 | 6% |
| Spain | 1 | 6% |
| South Africa | 1 | 6% |
| Unknown | 7 | 39% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 14 | 78% |
| Scientists | 4 | 22% |
Mendeley readers
The data shown below were compiled from readership statistics for 451 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 9 | 2% |
| Germany | 3 | <1% |
| United Kingdom | 2 | <1% |
| Canada | 2 | <1% |
| France | 1 | <1% |
| Italy | 1 | <1% |
| Australia | 1 | <1% |
| Brazil | 1 | <1% |
| Sweden | 1 | <1% |
| Other | 9 | 2% |
| Unknown | 421 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 122 | 27% |
| Researcher | 96 | 21% |
| Student > Master | 59 | 13% |
| Professor > Associate Professor | 25 | 6% |
| Student > Bachelor | 23 | 5% |
| Other | 71 | 16% |
| Unknown | 55 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 75 | 17% |
| Agricultural and Biological Sciences | 43 | 10% |
| Computer Science | 41 | 9% |
| Physics and Astronomy | 41 | 9% |
| Mathematics | 32 | 7% |
| Other | 135 | 30% |
| Unknown | 84 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 15. 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 28 October 2019.
All research outputs
#2,512,905
of 25,837,817 outputs
Outputs from Statistics and Computing
#29
of 648 outputs
Outputs of similar age
#5,488
of 92,356 outputs
Outputs of similar age from Statistics and Computing
#1
of 2 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 648 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done particularly well, scoring higher than 93% 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 92,356 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 2 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