Repeated observation models

Authors:
Avi Pfeffer
Affiliations:
Division of Engineering and Applied Sciences, Harvard University
Venue:
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Year:
2004

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Abstract

Repetition is an important phenomenon in a variety of domains, such as music, computer programs and architectural drawings. A generative model for these domains should account for the possibility of repetition. We present repeated observation models (ROMs), a framework for modeling sequences that explicitly allows for repetition. In a ROM, an element is either generated by copying a previous element, or by using a base model. We show how to build ROMs using n- grams and hidden Markov models as the base model. We also describe an extension of ROMs in which entire subsequences are repeated together. Results from a music modeling domain show that ROMs can lead to dramatic improvement in predictive ability.