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    On some integrated approaches to inference

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    Date Issued
    2012
    Author(s)
    Kon, Mark A.
    Plaskota, Leszek
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    Permanent Link
    https://hdl.handle.net/2144/38439
    Version
    First author draft
    Citation (published version)
    Mark A Kon, Leszek Plaskota. 2012. "On Some Integrated Approaches to Inference." CoRR, Volume abs/1212.1180.
    Abstract
    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.
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