Exploring scientific models using emulation

In this thesis, I study the potential of using emulation to explore the parameter space of scientific models. In particular, I focus on enhancing the efficiency of models that deal with low accuracy observational data sets; many approximations; and unknown physics. As a solution, I have created PRISM. PRISM is designed to easily facilitate and enhance existing Markov chain Monte Carlo methods by restricting plausible regions and exploring parameter space efficiently. With PRISM, the time spent on evaluating a model is minimized, providing developers with an advanced model analysis for a fraction of the time required by more traditional methods.