Error decomposition and model complexity.

[Huaiyu Zhu <zhuh@santafe.edu>]

The following paper has been submitted to Neural Computation:

http://www.santafe.edu/~zhuh/draft/edmc.ps.gz 

		Error Decomposition and Model Complexity

			     Huaiyu Zhu

  Bayesian information geometry provides a general error decomposition
  theorem for arbitrary statistical models and a family of information
  deviations that include Kullback-Leibler information as a special case.
  When applied to Gaussian measures it takes the classical Hilbert space
  (Sobolev space) theories for estimation (regression, filtering,
  approximation, smoothing) as a special case.  When the statistical and
  computational models are properly distinguished, the dilemmas of
  over-fitting and ``curse of dimensionality'' disappears, and the optimal
  model order disregarding computing cost is always infinity.


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