UC Berkeley

Statistical inference may be large-scale in terms of the size of the dataset, the dimension of the data, or the amount of data needed for provably accurate inference. This dissertation presents three applications of large-scale statistical inference. Part I considers finding and characterizing stars and galaxies in images from telescopes. Part II considers figuring out who wrote what in large collection of articles, where authors often do not have unique names. Part III considers approximating a high-dimensional function based on a small number of observations, a common problem when interpreting computer experiments.