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Mocapy++ - A toolkit for inference and learning in dynamic Bayesian networks

Overview of attention for article published in BMC Bioinformatics, March 2010
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1 X user

Citations

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70 Mendeley
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10 CiteULike
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Title
Mocapy++ - A toolkit for inference and learning in dynamic Bayesian networks
Published in
BMC Bioinformatics, March 2010
DOI 10.1186/1471-2105-11-126
Pubmed ID
Authors

Martin Paluszewski, Thomas Hamelryck

Abstract

Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distributions, including distributions from directional statistics (the statistics of angles, directions and orientations).

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 3 4%
Germany 2 3%
United States 2 3%
France 1 1%
Portugal 1 1%
Canada 1 1%
India 1 1%
Spain 1 1%
Belgium 1 1%
Other 0 0%
Unknown 57 81%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 24%
Student > Ph. D. Student 11 16%
Professor 8 11%
Professor > Associate Professor 7 10%
Student > Master 6 9%
Other 18 26%
Unknown 3 4%
Readers by discipline Count As %
Computer Science 22 31%
Agricultural and Biological Sciences 19 27%
Engineering 8 11%
Chemistry 6 9%
Biochemistry, Genetics and Molecular Biology 4 6%
Other 7 10%
Unknown 4 6%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 20 April 2012.
All research outputs
#15,243,120
of 22,664,644 outputs
Outputs from BMC Bioinformatics
#5,359
of 7,247 outputs
Outputs of similar age
#76,568
of 93,735 outputs
Outputs of similar age from BMC Bioinformatics
#38
of 56 outputs
Altmetric has tracked 22,664,644 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,247 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
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 93,735 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 56 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.