Name
Title | ||
|---|---|---|
Improved numerical inversion methods for the recovery of bivariate distributions of polymer properties from 2D probability generating function domains. |
Abstract | ||
|---|---|---|
In this work two 2D pgf inversion methods are developed, for which the pgf is regarded as a complex variable. These methods provide an outstanding accuracy in the inversion, thus allowing extending the 2D pgf technique for modeling bivariate distributions without restrictions in the range of values of its independent domains. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.compchemeng.2016.07.017 | Computers & Chemical Engineering |
Keywords | Field | DocType |
Modeling,Polymerization,Bivariate distribution,2D probability generating function | Orders of magnitude (numbers),Generating function,Population,Branching (polymer chemistry),Mathematical optimization,Joint probability distribution,Inversion (meteorology),Bivariate analysis,Probability-generating function,Mathematics | Journal |
Volume | ISSN | Citations |
94 | 0098-1354 | 1 |
PageRank | References | Authors |
0.43 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adriana Brandolin | 1 | 2 | 1.18 |
| Ayslane Assini Balbueno | 2 | 1 | 0.43 |
| Mariano Asteasuain | 3 | 2 | 1.52 |