Emulation methods to improve inference performance for complex models

这个项目将研究使用模拟困难的可能性景观，以加快推理算法。方法将被开发来引入一个多元近似的似然函数，可以显着快于封闭形式的可能性进行评估。该方法将被应用到复杂的蛋白质荧光模型的推理。所得到的方法将被用于参数推断和模型排名在几个案例研究中，利用时间分辨荧光实验的数据在地球物理学。

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.