Journal
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING
Volume 21, Issue 10, Pages 827-855Publisher
JOHN WILEY & SONS LTD
DOI: 10.1002/acs.949
Keywords
non-parametric process; non-parametric sequential stopping rule; non-stationary Dirichlet process; non-parametric stabilized forgetting
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The Dirichlet process prior (DPP) is used to model an unknown probability distribution, F. This eliminates the need for parametric model assumptions, providing robustness in problems where there is significant model uncertainty. Two important parametric techniques for learning are extended to this non-parametric context for the first time. These are (i) sequential stopping, which proposes an optimal stopping time for online learning of F using i.i.d. sampling; and (ii) stabilized forgetting, which updates the DPP in response to changes in F, but without the need for a formal transition model. In each case, a practical and highly tractable algorithm is revealed, and simulation studies are reported. Copyright (C) 2007 John Wiley & Sons, Ltd.
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