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Universal Principles, Approximation and Model Choices

This video was recorded at Workshop on Modelling in Classification and Statistical Learning, Eindhoven 2004. Universal principles are ones which make no reference to the subject matter of the data and include Maximum Likelihood, Bayes, AIC and MDL. In this talk we criticize the use of such principles to solve the problem of model choice. The criticism will be mainly directed against MDL but corresponding arguments can be made against the other principles. A concept of approximation will be introduced and its use in choosing a model illustrated by examples from non-parametric statistics.

Keywords:: videolectures, ocwc, oec

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Science and Technology / Computer Science / Programming & Programming Languages

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Material Type:: Presentation
Date Added to MERLOT:: February 10, 2015
Date Modified in MERLOT:: February 10, 2015
Author:: Lauri Davies, University of Duisburg-Essen
Submitter:: The Open Education Consortium
Primary Audience:: College General Ed, College Lower Division, College Upper Division
Technical Format:: Video

Mobile Compatibility:: Not specified at this time
Language:: English
Cost Involved:: No
Source Code Available:: No
Creative Commons:: This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States