Bayesian optimization of hyperparameters from noisy marginal likelihood estimates
基于含噪边际似然估计的贝叶斯超参数优化
Summary Bayesian models often involve a small set of hyperparameters determined by maximizing the marginal likelihood. Bayesian optimization is an iterative method where a Gaussian process posterior of the underlying function is sequentially updated by new function evaluations. We propose a novel Bayesian optimization framework for situations where the user controls the computational effort and therefore the precision of the function evaluations. This is a common situation…