Example of the results of the model fitting in the 103-dimensional parameter space. by Kevin J. Wischnewski (21354521)

Model-based sex classification accuracies. by Kevin J. Wischnewski (21354521)

A robust method to simultaneously place sensors and calibrate parameters for urban drainage pipe system models using Bayesian decision theory by Yuan Huang (79836)

“…Subsequently, Bayesian Experimental Design is employed to identify optimal sensor locations by maximizing expected data worth, measured by the relative entropy between prior and posterior probabilities. …”

Supplementary information for Efficient distributed edge computing for dependent delay-sensitive tasks in multi-operator multi-access networks by Alia Asheralieva (17562462)

“…We prove that the game has a perfect Bayesian equilibrium (PBE) yielding unique optimal values, and formulate new Bayesian reinforcement learning and Bayesian deep reinforcement learning algorithms enabling each PN to reach the PBE autonomously (without communicating with other PNs).…”

Hyperparameters for the XGBoost model. by Hoa Thi Trinh (20347834)

“…The study used the largest database of RC walls to date, consisting of 1057 samples with various cross-sectional shapes. Bayesian optimization (BO) algorithms, including BO—Gaussian Process, BO—Random Forest, and Random Search methods, were used to refine the XGBoost model architecture. …”

Data from Fig 3. by Hoa Thi Trinh (20347834)

“…The study used the largest database of RC walls to date, consisting of 1057 samples with various cross-sectional shapes. Bayesian optimization (BO) algorithms, including BO—Gaussian Process, BO—Random Forest, and Random Search methods, were used to refine the XGBoost model architecture. …”

Distribution of cross-section stypes. by Hoa Thi Trinh (20347834)

“…The study used the largest database of RC walls to date, consisting of 1057 samples with various cross-sectional shapes. Bayesian optimization (BO) algorithms, including BO—Gaussian Process, BO—Random Forest, and Random Search methods, were used to refine the XGBoost model architecture. …”

Example of data used in Table 1. by Hoa Thi Trinh (20347834)

“…The study used the largest database of RC walls to date, consisting of 1057 samples with various cross-sectional shapes. Bayesian optimization (BO) algorithms, including BO—Gaussian Process, BO—Random Forest, and Random Search methods, were used to refine the XGBoost model architecture. …”

Data from Fig 7. by Hoa Thi Trinh (20347834)

“…The study used the largest database of RC walls to date, consisting of 1057 samples with various cross-sectional shapes. Bayesian optimization (BO) algorithms, including BO—Gaussian Process, BO—Random Forest, and Random Search methods, were used to refine the XGBoost model architecture. …”

Data from Fig 8. by Hoa Thi Trinh (20347834)

“…The study used the largest database of RC walls to date, consisting of 1057 samples with various cross-sectional shapes. Bayesian optimization (BO) algorithms, including BO—Gaussian Process, BO—Random Forest, and Random Search methods, were used to refine the XGBoost model architecture. …”

Data from Fig 4. by Hoa Thi Trinh (20347834)

“…The study used the largest database of RC walls to date, consisting of 1057 samples with various cross-sectional shapes. Bayesian optimization (BO) algorithms, including BO—Gaussian Process, BO—Random Forest, and Random Search methods, were used to refine the XGBoost model architecture. …”

Features of shear strength database for RC walls. by Hoa Thi Trinh (20347834)

“…The study used the largest database of RC walls to date, consisting of 1057 samples with various cross-sectional shapes. Bayesian optimization (BO) algorithms, including BO—Gaussian Process, BO—Random Forest, and Random Search methods, were used to refine the XGBoost model architecture. …”

A Local Variational Inference Framework for the Orthogonal Gaussian Process Calibration by Jingru Huang (13946955)

ISO-NE Load Data (January 2024–October 2024). by Nasir Nauman (22272299)

“…The second, GA-LSTM, integrates Long Short-Term Memory with genetic algorithm-based optimization to improve forecasting accuracy and robustness. …”

Impact of temperature on price and load. by Nasir Nauman (22272299)

“…The second, GA-LSTM, integrates Long Short-Term Memory with genetic algorithm-based optimization to improve forecasting accuracy and robustness. …”

Relationship between ISO-NE Price and Load. by Nasir Nauman (22272299)

“…The second, GA-LSTM, integrates Long Short-Term Memory with genetic algorithm-based optimization to improve forecasting accuracy and robustness. …”

ISO-NE Price Data (January 2024–October 2024). by Nasir Nauman (22272299)

“…The second, GA-LSTM, integrates Long Short-Term Memory with genetic algorithm-based optimization to improve forecasting accuracy and robustness. …”

Comparison of forecasting errors for ISO-NE. by Nasir Nauman (22272299)

“…The second, GA-LSTM, integrates Long Short-Term Memory with genetic algorithm-based optimization to improve forecasting accuracy and robustness. …”

Impact of Wind Speed on Price and Load. by Nasir Nauman (22272299)

“…The second, GA-LSTM, integrates Long Short-Term Memory with genetic algorithm-based optimization to improve forecasting accuracy and robustness. …”

<b>Spatial modeling of gully density on the Qinghai-Tibet Plateau: Application of hyperparameter optimization in interpretable machine learning</b> by Zhoujiang Liu (22741966)

“…Various machine learning models were used, and different hyperparameter optimization algorithms were selected to train the models to obtain the best model. …”