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Full-Text Articles in Engineering
A Bayesian Nonparametric Multiple Testing Procedure For Comparing Several Treatments Against A Control, Luis Gutiérrez, Andrés Barrientos, Jorge González, Daniel Taylor-Rodríguez
A Bayesian Nonparametric Multiple Testing Procedure For Comparing Several Treatments Against A Control, Luis Gutiérrez, Andrés Barrientos, Jorge González, Daniel Taylor-Rodríguez
Mathematics and Statistics Faculty Publications and Presentations
We propose a Bayesian nonparametric strategy to test for differences between a control group and several treatment regimes. Most of the existing tests for this type of comparison are based on the differences between location parameters. In contrast, our approach identifies differences across the entire distribution, avoids strong modeling assumptions over the distributions for each treatment, and accounts for multiple testing through the prior distribution on the space of hypotheses. The proposal is compared to other commonly used hypothesis testing procedures under simulated scenarios. Two real applications are also analyzed with the proposed methodology.
Inertial And Time-Of-Arrival Ranging Sensor Fusion, Paul Vasilyeav, Sean Pearson, Mahmoud El-Gohary, Mateo Aboy, James Mcnames
Inertial And Time-Of-Arrival Ranging Sensor Fusion, Paul Vasilyeav, Sean Pearson, Mahmoud El-Gohary, Mateo Aboy, James Mcnames
Electrical and Computer Engineering Faculty Publications and Presentations
Wearable devices with embedded kinematic sensors including triaxial accelerometers, gyroscopes, and magnetometers are becoming widely used in applications for tracking human movement in domains that include sports, motion gaming, medicine, and wellness. The kinematic sensors can be used to estimate orientation, but can only estimate changes in position over short periods of time. We developed a prototype sensor that includes ultra wideband ranging sensors and kinematic sensors to determine the feasibility of fusing the two sensor technologies to estimate both orientation and position. We used a state space model and applied the unscented Kalman filter to fuse the sensor information. …
Improved Bayesian Multi-Modeling: Integration Of Copulas And Bayesian Model Averaging, Shahrbanou Madadgar, Hamid Moradkhani
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
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 …