## Innovations in Bayesian Networks: Theory and ApplicationsBayesian networks currently provide one of the most rapidly growing areas of research in computer science and statistics. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. Each of the twelve chapters is self-contained. Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Graduate students since it shows the direction of current research. |

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### Contents

Introduction to Bayesian Networks | 1 |

A Polemic for Bayesian Statistics | 6 |

A Tutorial on Learning with Bayesian Networks | 33 |

The Causal Interpretation of Bayesian Networks | 83 |

An Introduction to Bayesian Networks and Their Contemporary Applications | 117 |

Objective Bayesian Nets for Systems Modelling and Prognosis in Breast Cancer | 131 |

Modeling the Temporal Trend of the Daily Severity of an Outbreak Using Bayesian Networks | 169 |

An InformationGeometric Approach to Learning Bayesian Network Topologies from Data | 186 |

Causal Graphical Models with Latent Variables Learning and Inference | 219 |

Use of Explanation Trees to Describe the State Space of a ProbabilisticBased Abduction Problem | 250 |

Toward a Generalized Bayesian Network | 281 |

A Survey of FirstOrder Probabilistic Models | 289 |

318 | |

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algorithm ancestral graphs approach approximation arguments Artiﬁcial Intelligence Bayesian nets Bayesian networks belief breast cancer causal discovery causal inference causal model clinical complex compute conditional independence conditional probabilities conﬁdence interval conﬁguration constraints d-separation data set deﬁne Deﬁnition density function diﬀerent directed acyclic graph discuss domain edges eﬀect eﬃcient encode epidemic curve Equation estimate example experiments explanation ﬁnd ﬁnding ﬁrst ﬁrst-order frequentist genomic given graph graphical models Heckerman hidden variables hypothesis independence inducing path inﬂuence inﬂuenza instantiated intervention joint probability knowledge latent variables learning likelihood logic programming marginal likelihood Markov Minimum Description Length network structure node objective Bayesian obnet observable variables obtained ok ok ok outbreak parameters Pearl possible posterior probability prior probabilistic dependence probabilistic inference probabilistic logic probability distribution problem random variables relationships represent result sample score signiﬁcance SMCM speciﬁc Spirtes Statistics suﬃcient techniques theory topologies tree Uncertainty in Artiﬁcial