Explores Probabilistic Linear Regression and Gaussian Process Regression, emphasizing kernel selection and hyperparameter tuning for accurate predictions.
Explores verification and validation in computational modeling, emphasizing accuracy through comparison with experimental data and practical advice on model complexity.
