Prequential Plug-In Codes that Achieve Optimal Redundancy Rates even if the Model is Wrong

Additional Metadata
Keywords	information theory
MSC	Information theory, general (msc 94A15)
THEME	Life Sciences (theme 5), Logistics (theme 3)
Publisher	IEEE
Project	Learning when all models are wrong
Conference	IEEE International Symposium on Information Theory
Organisation	Algorithms and Complexity
Citation APA APA Style APA-ALL Style AAA Style Cell Style Chicago Style Harvard Style IEEE Style MLA Style Nature Style Vancouver Style American-Institute-of-Physics Style Council-of-Science-Editors Style BibTex Format Endnote Format RIS Format CSL Format DOIs only Format	Kotlowski, W.& Grünwald, P. (2010, July). Prequential Plug-In Codes that Achieve Optimal Redundancy Rates even if the Model is Wrong.

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