Automated Author ProfileBeliakov, Gleb
Beliakov, Gleb
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 4.5 (sum of 4 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Abstract This paper describes generation of nonuniform random variates from Lipschitz-continuous densities using acceptance/rejection, and the class library ranlip which implements this method. It is assumed that the required distribution has Lipschitz-continuous density, which is either given analytically or as a black box. The algorithm builds a piecewise constant upper approximation to the density (the hat function), using a large number of its values and subdivision of the domain into hyperrectang... Title of program: Ranlip Catalogue Id: ADVP_v1_0 Nature of problem This program allows one to generate nonuniform random vectors from a variety of distributions (especially multimodal), using acceptance/rejection approach. Suitable for non-standard distributions for up to five variables. Versions of this program held in the CPC repository in Mendeley Data ADVP_v1_0; Ranlip; 10.1016/j.cpc.2005.03.105 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018)
Authors
- Beliakov, Gleb
Abstract This paper describes generation of nonuniform random variates from Lipschitz-continuous densities using acceptance/rejection, and the class library ranlip which implements this method. It is assumed that the required distribution has Lipschitz-continuous density, which is either given analytically or as a black box. The algorithm builds a piecewise constant upper approximation to the density (the hat function), using a large number of its values and subdivision of the domain into hyperrectang... Title of program: Ranlip Catalogue Id: ADVP_v1_0 Nature of problem This program allows one to generate nonuniform random vectors from a variety of distributions (especially multimodal), using acceptance/rejection approach. Suitable for non-standard distributions for up to five variables. Versions of this program held in the CPC repository in Mendeley Data ADVP_v1_0; Ranlip; 10.1016/j.cpc.2005.03.105 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018)
Authors
- Beliakov, Gleb
No description available
Authors
- Beliakov, Gleb ;
- Matiyasevich, Yuri