Probabilistic Programming Framework for Scientific Modeling

Develop a sophisticated probabilistic programming framework designed for complex scientific modeling scenarios. Create a system that supports Bayesian inference, provides intuitive model specification interfaces, and enables efficient posterior sampling. Implement advanced probabilistic programming constructs, support for hierarchical models, and comprehensive uncertainty quantification. Design the framework to handle challenges in scientific domains like climate modeling, epidemiology, and experimental physics.