Altmetric
bounded-rand-walkers
There are no files associated with this item.
| Title: | bounded-rand-walkers |
| Authors: | Kuhn-Regnier, A Cocconi, L Neuss, M |
| Item Type: | Software / Code |
| Abstract: | This is the first release of the software used in our paper investigating bounded random walks.
We implemented an adaptive rejection sampling algorithm in both Python and C++, allowing the investigation of bounded random walks given user-specified intrinsic step distributions and convex geometries. Multiple binning techniques are used throughout in order to enable analysis of both 2D gridded and 1D radially-averaged data, and a custom integrator is used to achieve high numerical accuracy where needed. This is the first release of the software used in our paper investigating bounded random walks. We implemented an adaptive rejection sampling algorithm in both Python and C++, allowing the investigation of bounded random walks given user-specified intrinsic step distributions and convex geometries. Multiple binning techniques are used throughout in order to enable analysis of both 2D gridded and 1D radially-averaged data, and a custom integrator is used to achieve high numerical accuracy where needed. |
| Content Version: | 1.0.0 |
| Issue Date: | 22-Oct-2020 |
| URI: | http://hdl.handle.net/10044/1/90094 |
| DOI: | https://doi.org/10.5281/zenodo.4119096 |
| Copyright Statement: | https://opensource.org/licenses/MIT |
| Keywords: | bounded-rand-walkers |
| Appears in Collections: | Faculty of Natural Sciences - Research Data |