## Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis: A Guide to Construction and Analysis (Google eBook)Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification, troubleshooting, and data mining under uncertainty.
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Intended primarily for practitioners, this book does not require sophisticated mathematical skills or deep understanding of the underlying theory and methods nor does it discuss alternative technologies for reasoning under uncertainty. The theory and methods presented are illustrated through more than 140 examples, and exercises are included for the reader to check his/her level of understanding.
The techniques and methods presented for knowledge elicitation, model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models have all been developed and refined on the basis of numerous courses that the authors have held for practitioners worldwide. |

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### Contents

3 | |

Bayesian Networks | 9 |

Concluding Remarks | 15 |

Causality | 24 |

Two Equivalent Irrelevance Criteria | 31 |

robabilities | 37 |

Probability Potentials | 44 |

Fundamental Rule and Bayes Rule | 50 |

Model Verification | 159 |

Concluding Remarks | 170 |

Modeling Techniques | 177 |

Probability Distribution Related Techniques | 196 |

Decision Related Techniques | 212 |

Summary | 225 |

Sequential Parameter Learning | 252 |

Conflict Analysis 261 | 259 |

### Common terms and phrases

algorithm Apple Jack Assume barren variable belief bility distribution Bronchitis Burglary causal influence cause Chapter CLG Bayesian network compute conditional independence conditional probability distribution configuration consider continuous variables d-separation decision maker decision options decision problem decision variables defined dependence and independence dependence relations directed directed graph discrete random variable discrete variables dom(X Dyspnoea Earthquake edge elicitation elimination example expected utility false Gaussian given golf graph graphical gure hypothesis driven hypothesis variable identify idiom impact inference influence diagram instance instantiation joint probability distribution junction tree knowledge Lauritzen LIMID marginal maximum expected utility model construction modeling technique network class nodes observations oil wildcatter parameter possible posterior probability potential probabilistic network probability nution problem domain representation rule Section Seismic sensitivity set of evidence set of variables shown in Figure Sick solving Spark_plugs specified structure subset Table true uncertainty undirected utility function