## Uncertain Reasoning and Forecasting (1995)

Venue: | International Journal of Forecasting |

Citations: | 21 - 3 self |

### BibTeX

@ARTICLE{Dagum95uncertainreasoning,

author = {Paul Dagum and Adam Galper and Eric Horvitz and Adam Seiver},

title = {Uncertain Reasoning and Forecasting},

journal = {International Journal of Forecasting},

year = {1995},

volume = {11},

pages = {73--87}

}

### OpenURL

### Abstract

We develop a probability forecasting model through a synthesis of Bayesian beliefnetwork models and classical time-series analysis. By casting Bayesian time-series analyses as temporal belief-network problems, weintroduce dependency models that capture richer and more realistic models of dynamic dependencies. With richer models and associated computational methods, we can movebeyond the rigid classical assumptions of linearityin the relationships among variables and of normality of their probability distributions.

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Citation Context ...rtain knowledge. A Bayesian belief network is an expressive knowledge representation for uncertain reasoning that employs a graphical structure to capture explicit dependencies among domain variables =-=[35, 36]-=-. Belief networks are the basis of diagnostic systems for many real-world applications [2, 3, 7, 26, 28, 29, 42]. Techniques for probabilistic inference in belief networks [8, 12, 16, 27, 34] and for ... |

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Citation Context ...ng domain variables [35, 36]. Belief networks are the basis of diagnostic systems for many real-world applications [2, 3, 7, 26, 28, 29, 42]. Techniques for probabilistic inference in belief networks =-=[8, 12, 16, 27, 34]-=- and for speci cation of belief networks [11, 37, 38, 43] have emerged in response to the diversity of belief-network applications. More generally, applied statisticians have recognized the importance... |

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Citation Context ... and for temporal reasoning with belief networks and in uence diagrams [4, 44, 39] have been proposed. Real-world applications of forecasting with belief networks include forecasting crude-oil prices =-=[1]-=-. We have developed a probability forecasting model through a synthesis of belief-network models and classical time-series analysis [14, 15]. The dynamic network model (DNM) is based 2son the integrat... |

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Citation Context ...loped to address the forecasting problem directly. Several approaches for probabilistic reasoning about change over time [18, 30] and for temporal reasoning with belief networks and in uence diagrams =-=[4, 44, 39]-=- have been proposed. Real-world applications of forecasting with belief networks include forecasting crude-oil prices [1]. We have developed a probability forecasting model through a synthesis of beli... |

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Citation Context ...loped to address the forecasting problem directly. Several approaches for probabilistic reasoning about change over time [18, 30] and for temporal reasoning with belief networks and in uence diagrams =-=[4, 44, 39]-=- have been proposed. Real-world applications of forecasting with belief networks include forecasting crude-oil prices [1]. We have developed a probability forecasting model through a synthesis of beli... |

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Citation Context ...ve decomposition to improve the e ciency of inference. The inference algorithm for additive belief networks [13] is similar to Cooper's nested dissection algorithm for general probabilistic inference =-=[9]-=-. The algorithm decomposes the belief network into subnetworks using the additive decompositions. The Lauritzen-Speiglehalter (L-S) algorithm is used [34] to perform inference on the subnetworks. The ... |

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Citation Context ...loped to address the forecasting problem directly. Several approaches for probabilistic reasoning about change over time [18, 30] and for temporal reasoning with belief networks and in uence diagrams =-=[4, 44, 39]-=- have been proposed. Real-world applications of forecasting with belief networks include forecasting crude-oil prices [1]. We have developed a probability forecasting model through a synthesis of beli... |

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Citation Context ...f patient care. Systems to predict patient care would limit admission to the ICU to patients who would clearly bene t from critical care, and would withhold therapy from patients who would not bene t =-=[21]-=-. One system, the acute physiology and chronic health evaluation (APACHE) system, relates the severity ofa patient's illness to the degree of physiologic derangement of a set of physiologic measuremen... |

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