GOBNILP: Globally Optimal Bayesian Network learning using Integer Linear Programming

James Cussens; Mark Bartlett

GOBNILP: Globally Optimal Bayesian Network learning using Integer Linear Programming

James Cussens (Developer), Mark Bartlett (Developer)

Computer Science

Research output: Non-textual form › Software

Abstract

GOBNILP (Globally Optimal Bayesian Network learning using Integer Linear Programming) is a C program which learns Bayesian networks from complete discrete data or from local scores. The GOBNILP distribution provides a separate C program to generate BDeu local scores from complete discrete data. GOBNILP uses the SCIP framework for Constraint Integer Programming.

Original language	English
Publication status	Published - 2013

Access to Document

http://www.cs.york.ac.uk/aig/sw/gobnilp/

Cite this

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title = "GOBNILP: Globally Optimal Bayesian Network learning using Integer Linear Programming",

abstract = "GOBNILP (Globally Optimal Bayesian Network learning using Integer Linear Programming) is a C program which learns Bayesian networks from complete discrete data or from local scores. The GOBNILP distribution provides a separate C program to generate BDeu local scores from complete discrete data. GOBNILP uses the SCIP framework for Constraint Integer Programming.",

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M3 - Software

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