(learning probabilistic and possibilistic graphical models)
| lnxines.zip | Linux executables | (253 kb) | ||
| winines.zip | Windows console executables | (320 kb) | ||
| ines.zip | C sources, package version 3.5, 2008.08.11 | (176 kb) | ||
| ines.tar.gz | (151 kb) |
Attention: In order to compile these programs, the table package must also be retrieved. The table package also contains some auxiliary programs for preprocessing the data files.
Programs to learn a graphical model (Bayesian network or possibilistic network) from a dataset of sample cases, to generate a random dataset from a Bayesian network, to evaluate learned networks w.r.t. a test dataset and a reference network, and to measure the strengths of conditional dependences.
A brief description of how to apply these programs can be found in the file ines/ex/readme in the source package. The scripts djc_prob, djc_poss, and djc_local in the directory ines/djc may also be helpful.
The theory underlying this program is described in detail in the book:
Last updated: Thu Nov 06 18:06:06 CET 2008 - christian@borgelt.net
