Emulation methods to improve inference performance for complex models

This project will investigate using emulation of difficult likelihood landscapes to speed up inference algorithms. Methods will be developed to introduce a multivariate approximation to a likelihood function that can be evaluated significantly faster than a closed form likelihood. The methods will be applied to inference over complex models of protein fluorescence. Resulting methods will be used for parameter inference and model ranking in several case studies in photophysics using data from time resolved fluorescence experiments.