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Forecasting Sleep Apnea with Dynamic Network Models
Dynamic network models (DNMs) are belief networks for temporal reasoning...
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Additive Belief-Network Models
The inherent intractability of probabilistic inference has hindered the ...
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Dynamic Construction of Belief Networks
We describe a method for incrementally constructing belief networks. We ...
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ARCO1: An Application of Belief Networks to the Oil Market
Belief networks are a new, potentially important, class of knowledge-bas...
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A computational scheme for Reasoning in Dynamic Probabilistic Networks
A computational scheme for reasoning about dynamic systems using (causal...
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Rank Position Forecasting in Car Racing
Forecasting is challenging since uncertainty resulted from exogenous fac...
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Stable Multiple Time Step Simulation/Prediction from Lagged Dynamic Network Regression Models
Recent developments in computers and automated data collection strategie...
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Dynamic Network Models for Forecasting
We have developed a probabilistic forecasting methodology through a synthesis of belief network models and classical time-series analysis. We present the dynamic network model (DNM) and describe methods for constructing, refining, and performing inference with this representation of temporal probabilistic knowledge. The DNM representation extends static belief-network models to more general dynamic forecasting models by integrating and iteratively refining contemporaneous and time-lagged dependencies. We discuss key concepts in terms of a model for forecasting U.S. car sales in Japan.
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