The models used in inductive inference have their roots in the models used by the philosophers of science who were discussing the scientific method. The goal there, and in prior work in learning theory, was to come up with an explanation of the phenomenon under consideration. However, scientists rarely work directly for the grand goal of a complete explanation, seeking rather the more modest goal of finding features and facts about the observed data. In like manner, this project pursues several modifications to the traditional models used in inductive inference so as to study the pursuit of more modest scientific goals. This study is particularly relevant to contemporary science as automated data generation techniques produce sufficient volumes of data to overwhelm the analysis abilities of humans. The goal of our work is to illuminate precisely what can and cannot be accomplished by automatic data analysis algorithms.
