Learning

Cosmological Parameter Inference from Combined Datasets

Bayesian Inference and Probability Theory → Practical Implementation with Sampling Codes

A graduate-level learning path covering the statistical and computational techniques required to extract cosmological parameters from combined datasets. It progresses from probability theory and Bayesian inference through likelihood construction, sampling methods, and advanced topics such as parameter degeneracies, model comparison, and tension quantification.