## Introduction to Statistical Relational LearningHandling inherent uncertainty and exploiting compositional structure are fundamentalto understanding and designing large-scale systems. Statistical relational learning builds on ideasfrom probability theory and statistics to address uncertainty while incorporating tools from logic,databases and programming languages to represent structure. In Introduction to StatisticalRelational Learning, leading researchers in this emerging area of machine learning describe currentformalisms, models, and algorithms that enable effective and robust reasoning about richlystructured systems and data. The early chapters provide tutorials for material used in laterchapters, offering introductions to representation, inference and learning in graphical models, andlogic. The book then describes object-oriented approaches, including probabilistic relationalmodels, relational Markov networks, and probabilistic entity-relationship models as well aslogic-based formalisms including Bayesian logic programs, Markov logic, and stochastic logicprograms. Later chapters discuss such topics as probabilistic models with unknown objects,relational dependency networks, reinforcement learning in relational domains, and informationextraction. By presenting a variety of approaches, the book highlights commonalities and clarifiesimportant differences among proposed approaches and, along the way, identifies importantrepresentational and algorithmic issues. Numerous applications are provided throughout.Lise Getooris Assistant Professor in the Department of Computer Science at the University of Maryland. BenTaskar is Assistant Professor in the Computer and Information Science Department at the Universityof Pennsylvania. |

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#### Review: Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning Series)

User Review - Lurino - Goodreadsfor those who are interested in machine learning. very technical introductions, but not totally unapproachable. the content is more like a collection of papers about certain topics, and very ... Read full review

#### Review: Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning Series)

User Review - Chimezie Ogbuji - GoodreadsExcellent source for everything on the topic of statistical relational learning (Recommended by my AI / Machine Learning professor). Covers foundational topics, current research, and future topics as well. Read full review

### Contents

1 Introduction | 1 |

2 Graphical Models in a Nutshell | 13 |

3 Inductive Logic Programming in a Nutshell | 57 |

4 An Introduction to Conditional Random Fields for Relational Learning | 93 |

5 Probabilistic Relational Models | 129 |

6 Relational Markov Networks | 175 |

7 Probabilistic EntityRelationship Models PRMs and Plate Models | 201 |

8 Relational Dependency Networks | 239 |

Probabilistic Models with Unknown Objects | 373 |

A GeneralPurpose Probabilistic Language | 399 |

15 Lifted FirstOrder Probabilistic Inference | 433 |

16 Feature Generation and Selection in MultiRelational Statistical Learning | 453 |

With an Application in Mammography | 477 |

A PolicyLanguage Approach | 499 |

19 Statistical Relational Learning for Natural Language Information Extraction | 535 |

20 Global Inference for Entity and Relation Identification via a Linear Programming Formulation | 553 |