Uncertainty in Artificial Intelligence
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Blind Construction of Optimal Nonlinear Recursive Predictors for Discrete Sequences
Cosma Shalizi, Kristina Klinkner
We present a new method for nonlinear prediction of discrete random sequences under minimal structural assumptions. We give a mathematical construction for optimal predictors of such processes, in the form of hidden Markov models. We then describe an algorithm, CSSR (Causal-State Splitting Reconstruction), which approximates the ideal predictor from data. We discuss the reliability of CSSR, its data requirements, and its performance in simulations. Finally, we compare our approach to existing methods using variablelength Markov models and cross-validated hidden Markov models, and show theoretically and experimentally that our method delivers results superior to the former and at least comparable to the latter.
Pages: 504-511
PS Link:
PDF Link: /papers/04/p504-shalizi.pdf
AUTHOR = "Cosma Shalizi and Kristina Klinkner",
TITLE = "Blind Construction of Optimal Nonlinear Recursive Predictors for Discrete Sequences",
BOOKTITLE = "Proceedings of the Twentieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-04)",
ADDRESS = "Arlington, Virginia",
YEAR = "2004",
PAGES = "504--511"

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