Bayesian Model Selection and Emulation for Protein Fluorescence

Abstract

- Fluorescence decay of amino acids in protein is a complex process for which multiple models have been proposed. Likelihood function evaluation for certain models can be computationally expensive, and as such surrogate models may be introduced to speed up inference. In this paper, Gaussian processes are implemented in likelihood estimation of a range of models defined by convolutions of an initial excitation input and a decay function using both synthetic and real world data. Parameter inference and model selection using the surrogate models are performed and compared against the exact results. Model selection when incorporating surrogate models into the inference process is shown to be consistent.