title: BNFinder: exact and efficient method for learning Bayesian networks.
abstract: BACKGROUND
  Bayesian methods are widely used in many different areas of research. Recently, it has become a very popular tool for biological network reconstruction, due to its ability to handle noisy data. Even though there are many software packages allowing for Bayesian network reconstruction, only few of them are freely available to researchers. Moreover, they usually require at least basic programming abilities, which restricts their potential user base. Our goal was to provide software which would be freely available, efficient and usable to non-programmers.
  
  
  RESULTS
  We present a BNFinder software, which allows for Bayesian network reconstruction from experimental data. It supports dynamic Bayesian networks and, if the variables are partially ordered, also static Bayesian networks. The main advantage of BNFinder is the use exact algorithm, which is at the same time very efficient (polynomial with respect to the number of observations).
authors.affiliation: Institute of Informatics, University of Warsaw, Poland.
authors.forename: Bartek
authors.initials: B
authors.lastname: Wilczyński
authors.forename: Norbert
authors.initials: N
authors.lastname: Dojer
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D016247:Information Storage and Retrieval / Q000379:methods; D012984:Software