Bayesian Uncertainty Quantification in Experimental Datasets

Design a probabilistic modeling framework for quantifying measurement uncertainties in scientific experiments with high-dimensional, noisy datasets. Develop a Bayesian inference approach that can handle complex error propagation, incorporate prior knowledge, and generate probabilistic distributions of experimental outcomes. Include techniques for marginal likelihood estimation, model comparison, and uncertainty visualization across different scientific domains.