Comprehensive Uncertainty Quantification Framework

Create an advanced uncertainty quantification system capable of providing rigorous probabilistic estimates across different machine learning models and data scenarios. Develop a multi-method approach incorporating Bayesian inference, bootstrap resampling, and ensemble uncertainty estimation techniques. Design a flexible pipeline that can handle various model types, generate calibrated prediction intervals, and provide comprehensive uncertainty metrics. Include automated reporting, sensitivity analysis, and visualization of uncertainty landscapes.