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Christian Borgelt's Web Pages

INeS - Induction of Network Structures

(learning probabilistic and possibilistic graphical models)


32 bit 64 bit (32/64 bit only for executable) (592 kb) (697 kb) GNU/Linux executables (446 kb) (495 kb) Windows console executables (289 kb) ines.tar.gz (245 kb) C sources, package version 4.1 (2014.10.24)

Note: The table package contains some auxiliary programs for preprocessing the data files.


INeS (Induction of Networks Structures) is a set of 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.

If you have trouble executing the programs on Microsoft Windows, check whether you have the Microsoft Visual C++ Redistributable for Visual Studio 2017 (see under "Other Tools and Frameworks") installed, as the programs were compiled with Microsoft Visual Studio 2017.

The theory underlying this program is described in detail in the books:

Note: The learning results reported in these books were produced on a GNU/Linux system using the rand48 random number generator, which is not available on a Microsoft Windows System (but should be available on an Apple OSX system). In order to avoid different results on different systems, the above programs now use a different random number generator by default (see util/src/random.[ch]). However, on a GNU/Linux system, the results reported in the books can still be reproduced exactly by recompiling the programs with

make all ADDFLAGS="-DRAND_LIB48"

which compiles the programs in such a way that they use the rand48 random number generator.