# Learning Markov networks networks with context-specific independences.

Title | Learning Markov networks networks with context-specific independences. |

Publication Type | Journal Article |

Year of Publication | 2014 |

Authors | Edera A, Schlüter F, Bromberg F |

Journal | International Journal on Artificial Intelligence Tools |

Volume | 23 |

Issue | 06 |

Start Page | 1460030 |

Date Published | 12/2014 |

ISSN | ISSN: 0218-2130 |

Keywords | CSI models, Knowledge discovery, Markov networks; structure learning; context-specific independences |

Abstract | This work focuses on learning the structure of Markov networks from data. Markov networks are parametric models for compactly representing complex probability distributions. These models are composed by: a structure and numerical weights, where the structure describes independences that hold in the distribution. Depending on which is the goal of structure learning, learning algorithms can be divided into: density estimation algorithms, where structure is learned for answering inference queries; and knowledge discovery algorithms, where structure is learned for describing independences qualitatively. The latter algorithms present an important limitation for describing independences because they use a single graph; a coarse grain structure representation which cannot |

URL | http://www.worldscientific.com/doi/abs/10.1142/S0218213014600306 |

DOI | 10.1142/S0218213014600306 |