## Advances in Probabilistic Graphical ModelsIn recent years considerable progress has been made in the area of probabilistic graphical models, in particular Bayesian networks and influence diagrams. Probabilistic graphical models have become mainstream in the area of uncertainty in artificial intelligence; This carefully edited book brings together in one volume some of the most important topics of current research in probabilistic graphical modelling, learning from data and probabilistic inference. This includes topics such as the characterisation of conditional |

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### Contents

3 | |

A Causal Algebra for Dynamic Flow Networks | 39 |

Graphical and Algebraic Representatives of Conditional | 55 |

Bayesian Network Models with Discrete and Continuous | 81 |

Sensitivity Analysis of Probabilistic Networks | 103 |

A Review on Distinct Methods and Approaches to Perform | 127 |

Decisiveness in Loopy Propagation | 153 |

Lazy Inference in Multiply Sectioned Bayesian Networks | 174 |

Learning of Latent Class Models by Splitting and Merging | 235 |

An Efficient Exhaustive Anytime Sampling Algorithm | 253 |

Daniel GarciaSanchez Marek J Druzdzel 255 | 275 |

Parallel Markov Decision Processes | 295 |

Applications of HUGIN to Diagnosis and Control | 313 |

Biomedical Applications of Bayesian Networks | 333 |

Learning and Validating Bayesian Network Models of Gene | 359 |

The Role of Background Knowledge in Bayesian Classification | 376 |

A Study on the Evolution of Bayesian Network Graph | 193 |

Learning Bayesian Networks with an Approximated MDL | 214 |

### Other editions - View all

Advances in Probabilistic Graphical Models Peter Lucas,José A. Gámez,Antonio Salmerón Cerdan No preview available - 2010 |

Advances in Probabilistic Graphical Models Peter Lucas,José A. Gámez,Antonio Salmerón Cerdan No preview available - 2009 |

Advances in Probabilistic Graphical Models Peter Lucas,José A. Gámez,Antonio Salmerón Cerdan No preview available - 2007 |