Mixability in Statistical Learning

Date

2012

Authors

van Erven, Tim
Grunwald, Peter
Reid, Mark
Williamson, Robert

Journal Title

Journal ISSN

Volume Title

Publisher

Neural Information Processing Systems Foundation

Abstract

Statistical learning and sequential prediction are two different but related formalisms to study the quality of predictions. Mapping out their relations and transferring ideas is an active area of investigation. We provide another piece of the puzzle by s

Description

Keywords

Keywords: Active area; Bayesian inference; Convergence theorem; Fast rate; Minimum description length; Quality of predictions; Sequential prediction; Statistical learning; Bayesian networks; Inference engines; Stochastic systems; Forecasting

Citation

URI

http://hdl.handle.net/1885/65142

Collections

ANU Research Publications

Source

NEURAL INFORMATION PROCESSING SYSTEMS. Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012

Type

Conference paper

Book Title

Entity type

Access Statement

Open Access

License Rights

DOI

Restricted until

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Description
01_van Erven_Mixability_in_Statistical_2012.pdf (318.07 KB)

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