Nonparametric Estimation and On-Line Prediction for General Stationary Ergodic Sources
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We proposed a learning algorithm for nonparametric estimation and on-line prediction for general stationary ergodic sources. We prepare histograms each of which estimates the probability as a finite distribution, and mixture them with weights to construct an estimator. The whole analysis is based on measure theory. The estimator works whether the source is discrete or continuous. If it is stationary ergodic, then the measure theoretically given Kullback-Leibler information divided by the sequence length n converges to zero as n goes to infinity. In particular, for continuous sources, the method does not require existence of a probability density function.
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