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Portland State University

Series

2014

Bayesian statistical decision theory

Articles 1 - 2 of 2

Full-Text Articles in Engineering

Improved Bayesian Multi-Modeling: Integration Of Copulas And Bayesian Model Averaging, Shahrbanou Madadgar, Hamid Moradkhani Nov 2014

Improved Bayesian Multi-Modeling: Integration Of Copulas And Bayesian Model Averaging, Shahrbanou Madadgar, Hamid Moradkhani

Civil and Environmental Engineering Faculty Publications and Presentations

Bayesian Model Averaging (BMA) is a popular approach to combine hydrologic forecasts from individual models, and characterize the uncertainty induced by model structure. In the original form of BMA, the conditional probability density function (PDF) of each model is assumed to be a particular probability distribution (e.g. Gaussian, gamma, etc.). If the predictions of any hydrologic model do not follow certain distribution, a data transformation procedure is required prior to model averaging. Moreover, it is strongly recommended to apply BMA on unbiased forecasts, whereas it is sometimes difficult to effectively remove bias from the predictions of complex hydrologic models. To …


Toward A Reliable Prediction Of Seasonal Forecast Uncertainty: Addressing Model And Initial Condition Uncertainty With Ensemble Data Assimilation And Sequential Bayesian Combination, Caleb Matthew Dechant, Hamid Moradkhani Jun 2014

Toward A Reliable Prediction Of Seasonal Forecast Uncertainty: Addressing Model And Initial Condition Uncertainty With Ensemble Data Assimilation And Sequential Bayesian Combination, Caleb Matthew Dechant, Hamid Moradkhani

Civil and Environmental Engineering Faculty Publications and Presentations

Uncertainties are an unfortunate yet inevitable part of any forecasting system. Within the context of seasonal hydrologic predictions, these uncertainties can be attributed to three causes: imperfect characterization of initial conditions, an incomplete knowledge of future climate and errors within computational models. This study proposes a method to account for all threes sources of uncertainty, providing a framework to reduce uncertainty and accurately convey persistent predictive uncertainty. In currently available forecast products, only a partial accounting of uncertainty is performed, with the focus primarily on meteorological forcing. For example, the Ensemble Streamflow Prediction (ESP) technique uses meteorological climatology to estimate …