Abstract

A common practice in statistics and machine learning is to assume that the statistical data ty (e.g., ordinal, categorical or real-valued) of var ables, and usually also the likelihood model, is known. However, as the availability of realworld data increases, this assumption becomes too restrictive. Data are often heterogeneous, complex, and improperly or incompletely documented. Surprisingly, despite their practical importance, there is still a lack of tools to automatically discover the statistical types of, as well as appropriate likelihood (noise) models for, the variables in a dataset. In this paper, we fill this gap by proposing a Bayesian method, which accurately discovers the statistical data types in both synthetic and real data.