Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6963246 | Environmental Modelling & Software | 2015 | 9 Pages |
Abstract
We present a continuous variable Bayesian networks modeling framework that integrates the graphical representation of a Bayesian networks model with empirical model-developing approach. Our model retains the Bayesian networks model's graphical representation of hypothesized causal connections among important variables and employs conventional statistical modeling approaches for establishing functional relationships among these variables. The modeling framework avoids discretizing continuous variables and the resulting models can be updated over time when new data are available or updated using local data to develop a site-specific model. We illustrate the modeling approach using a data for establishing nutrient criteria in streams and rivers in Ohio, U.S.A.
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Computer Science
Software
Authors
Song S. Qian, Robert J. Miltner,