Inclusion principle for statistical inference and learning
Xinjia Chen · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2013
In this paper, we propose a general approach for statistical inference and machine learning based on accumulated observational data. We demonstrate that a large class of machine learning problems can be formulated as the general problem of constructing random intervals with pre-specified coverage probabilities for the parameters of the model for the observational data. We show that the construction of such random intervals can be accomplished by comparing the endpoints of random intervals with confidence sequences for the parameters obtained from the observational data. Asymptotic results are obtained for such sequential methods.