Autocorrelation, a common characteristic of many datasets, refers to correlation between values of the same variable on related objects. It violates the critical assumption of in- stance independence that underlies most conventional models. In this paper, we provide an overview of research on autocorrelation in a number of fields with an emphasis on implications for relational learning, and outline a number of challenges and opportunities for model learning and inference.
|Title of host publication||ICML 2004 Workshop on Statistical Relational Learning and its Connections to Other Fields|
|Number of pages||8|
|Publication status||Published - 1 Jan 2004|