  We present arguments for the formulation of unified approach to different
standard continuous inference methods from partial information. It is claimed
that an explicit partition of information into a priori (prior knowledge) and a
posteriori information (data) is an important way of standardizing inference
approaches so that they can be compared on a normative scale, and so that
notions of optimal algorithms become farther-reaching. The inference methods
considered include neural network approaches, information-based complexity, and
Monte Carlo, spline, and regularization methods. The model is an extension of
currently used continuous complexity models, with a class of algorithms in the
form of optimization methods, in which an optimization functional (involving
the data) is minimized. This extends the family of current approaches in
continuous complexity theory, which include the use of interpolatory algorithms
in worst and average case settings.
